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@@ -36,3 +36,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 scannet200/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log filter=lfs diff=lfs merge=lfs -text
 scannet200/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log filter=lfs diff=lfs merge=lfs -text
 scannet200/semseg-pt-v3m1-1-ppt-extreme-with-alc-submit/train.log filter=lfs diff=lfs merge=lfs -text
+scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/train.log filter=lfs diff=lfs merge=lfs -text
+scannet/semseg-pt-v3m1-1-ppt-extreme-with-alc/train.log filter=lfs diff=lfs merge=lfs -text

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/__init__.py

@@ -0,0 +1,25 @@
+from .defaults import DefaultDataset, ConcatDataset
+from .builder import build_dataset
+from .utils import point_collate_fn, collate_fn
+
+# indoor scene
+from .s3dis import S3DISDataset
+from .scannet import ScanNetDataset, ScanNet200Dataset
+from .scannetpp import ScanNetPPDataset
+from .scannet_pair import ScanNetPairDataset
+from .arkitscenes import ArkitScenesDataset
+from .structure3d import Structured3DDataset
+from .alc import ARKitScenesLabelMakerConsensusDataset, ARKitScenesLabelMakerScanNet200Dataset
+from .scannetpp import ScanNetPPDataset
+
+# outdoor scene
+from .semantic_kitti import SemanticKITTIDataset
+from .nuscenes import NuScenesDataset
+from .waymo import WaymoDataset
+
+# object
+from .modelnet import ModelNetDataset
+from .shapenet_part import ShapeNetPartDataset
+
+# dataloader
+from .dataloader import MultiDatasetDataloader

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/alc.py

@@ -0,0 +1,161 @@
+import glob
+import os
+from collections.abc import Sequence
+from copy import deepcopy
+
+import numpy as np
+import torch
+from labelmaker.label_data import get_wordnet
+from torch.utils.data import Dataset
+
+from pointcept.utils.cache import shared_dict
+from pointcept.utils.logger import get_root_logger
+
+from .builder import DATASETS
+from .preprocessing.alc.preprocess_arkitscenes_labelmaker_consensus import get_wordnet_compact_mapping
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_20, VALID_CLASS_IDS_200
+from .transform import TRANSFORMS, Compose
+
+
+@DATASETS.register_module()
+class ARKitScenesLabelMakerConsensusDataset(Dataset):
+
+    label_key = "semantic_pseudo_gt_wn199"
+
+    def __init__(
+        self,
+        split="train",
+        data_root="data/alc",
+        transform=None,
+        ignore_index=-1,
+        test_mode=False,
+        test_cfg=None,
+        cache=False,
+        loop=1,
+    ):
+        super(ARKitScenesLabelMakerConsensusDataset, self).__init__()
+        self.get_class_to_id()
+
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.cache = cache
+        self.loop = loop if not test_mode else 1  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+
+        if test_mode:
+            self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+            self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+
+        self.ignore_index = ignore_index
+
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_list),
+                self.loop,
+                split,
+            )
+        )
+
+    def get_class_to_id(self):
+        self.class2id = get_wordnet_compact_mapping()[0]
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+
+        if not self.cache:
+            data = torch.load(data_path)
+        else:
+            data_name = data_path.replace(os.path.dirname(self.data_root), "").split(".")[0]
+            cache_name = "pointcept" + data_name.replace(os.path.sep, "-")
+            data = shared_dict(cache_name)
+
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        scene_id = data["scene_id"]
+        if self.label_key in data.keys():
+            segment = data[self.label_key].reshape(-1)
+        else:
+            segment = np.ones(coord.shape[0]) * -1
+        instance = np.ones(coord.shape[0]) * -1
+
+        data_dict = dict(
+            coord=coord,
+            color=color,
+            segment=segment,
+            instance=instance,
+            scene_id=scene_id,
+        )
+
+        if normal is not None:
+            data_dict["normal"] = normal
+
+        return data_dict
+
+    def get_data_name(self, idx):
+        return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        segment = data_dict.pop("segment")
+        data_dict = self.transform(data_dict)
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        input_dict_list = []
+        for data in data_dict_list:
+            data_part_list = self.test_voxelize(data)
+            for data_part in data_part_list:
+                if self.test_crop:
+                    data_part = self.test_crop(data_part)
+                else:
+                    data_part = [data_part]
+                input_dict_list += data_part
+
+        for i in range(len(input_dict_list)):
+            input_dict_list[i] = self.post_transform(input_dict_list[i])
+        data_dict = dict(fragment_list=input_dict_list, segment=segment, name=self.get_data_name(idx))
+        return data_dict
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ARKitScenesLabelMakerScanNet200Dataset(ARKitScenesLabelMakerConsensusDataset):
+    label_key = "semantic_pseudo_gt_scannet200"
+
+    def get_class_to_id(self):
+        self.class2id = np.array(VALID_CLASS_IDS_200)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/arkitscenes.py

@@ -0,0 +1,114 @@
+"""
+ArkitScenes Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_200
+
+
+@DATASETS.register_module()
+class ArkitScenesDataset(Dataset):
+    def __init__(
+        self,
+        split="Training",
+        data_root="data/ARKitScenesMesh",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+    ):
+        super(ArkitScenesDataset, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.loop = (
+            loop if not test_mode else 1
+        )  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+        self.class2id = np.array(VALID_CLASS_IDS_200)
+
+        if test_mode:
+            self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+            self.test_crop = TRANSFORMS.build(self.test_cfg.crop)
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_list), self.loop, split
+            )
+        )
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+        elif isinstance(self.split, list):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data = torch.load(self.data_list[idx % len(self.data_list)])
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        segment = np.zeros(coord.shape[0])
+        data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        data_idx = self.data_idx[idx % len(self.data_idx)]
+        return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        segment = data_dict.pop("segment")
+        data_dict = self.transform(data_dict)
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        input_dict_list = []
+        for data in data_dict_list:
+            data_part_list = self.test_voxelize(data)
+            for data_part in data_part_list:
+                data_part_list = self.test_crop(data_part)
+                input_dict_list += data_part_list
+
+        for i in range(len(input_dict_list)):
+            input_dict_list[i] = self.post_transform(input_dict_list[i])
+        return input_dict_list, segment
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/builder.py

@@ -0,0 +1,15 @@
+"""
+Dataset Builder
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+DATASETS = Registry("datasets")
+
+
+def build_dataset(cfg):
+    """Build datasets."""
+    return DATASETS.build(cfg)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/dataloader.py

@@ -0,0 +1,112 @@
+from functools import partial
+import weakref
+import torch
+import torch.utils.data
+
+import pointcept.utils.comm as comm
+from pointcept.datasets.utils import point_collate_fn
+from pointcept.datasets import ConcatDataset
+from pointcept.utils.env import set_seed
+
+
+class MultiDatasetDummySampler:
+    def __init__(self):
+        self.dataloader = None
+
+    def set_epoch(self, epoch):
+        if comm.get_world_size() > 1:
+            for dataloader in self.dataloader.dataloaders:
+                dataloader.sampler.set_epoch(epoch)
+        return
+
+
+class MultiDatasetDataloader:
+    """
+    Multiple Datasets Dataloader, batch data from a same dataset and mix up ratio determined by loop of each sub dataset.
+    The overall length is determined by the main dataset (first) and loop of concat dataset.
+    """
+
+    def __init__(
+        self,
+        concat_dataset: ConcatDataset,
+        batch_size_per_gpu: int,
+        num_worker_per_gpu: int,
+        mix_prob=0,
+        seed=None,
+    ):
+        self.datasets = concat_dataset.datasets
+        self.ratios = [dataset.loop for dataset in self.datasets]
+        # reset data loop, original loop serve as ratios
+        for dataset in self.datasets:
+            dataset.loop = 1
+        # determine union training epoch by main dataset
+        self.datasets[0].loop = concat_dataset.loop
+        # build sub-dataloaders
+        num_workers = num_worker_per_gpu // len(self.datasets)
+        self.dataloaders = []
+        for dataset_id, dataset in enumerate(self.datasets):
+            if comm.get_world_size() > 1:
+                sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+            else:
+                sampler = None
+
+            init_fn = (
+                partial(
+                    self._worker_init_fn,
+                    dataset_id=dataset_id,
+                    num_workers=num_workers,
+                    num_datasets=len(self.datasets),
+                    rank=comm.get_rank(),
+                    seed=seed,
+                )
+                if seed is not None
+                else None
+            )
+            self.dataloaders.append(
+                torch.utils.data.DataLoader(
+                    dataset,
+                    batch_size=batch_size_per_gpu,
+                    shuffle=(sampler is None),
+                    num_workers=num_worker_per_gpu,
+                    sampler=sampler,
+                    collate_fn=partial(point_collate_fn, mix_prob=mix_prob),
+                    pin_memory=True,
+                    worker_init_fn=init_fn,
+                    drop_last=True,
+                    persistent_workers=True,
+                )
+            )
+        self.sampler = MultiDatasetDummySampler()
+        self.sampler.dataloader = weakref.proxy(self)
+
+    def __iter__(self):
+        iterator = [iter(dataloader) for dataloader in self.dataloaders]
+        while True:
+            for i in range(len(self.ratios)):
+                for _ in range(self.ratios[i]):
+                    try:
+                        batch = next(iterator[i])
+                    except StopIteration:
+                        if i == 0:
+                            return
+                        else:
+                            iterator[i] = iter(self.dataloaders[i])
+                            batch = next(iterator[i])
+                    yield batch
+
+    def __len__(self):
+        main_data_loader_length = len(self.dataloaders[0])
+        return (
+            main_data_loader_length // self.ratios[0] * sum(self.ratios)
+            + main_data_loader_length % self.ratios[0]
+        )
+
+    @staticmethod
+    def _worker_init_fn(worker_id, num_workers, dataset_id, num_datasets, rank, seed):
+        worker_seed = (
+            num_workers * num_datasets * rank
+            + num_workers * dataset_id
+            + worker_id
+            + seed
+        )
+        set_seed(worker_seed)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/defaults.py

@@ -0,0 +1,297 @@
+"""
+Default Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+from collections.abc import Sequence
+
+from pointcept.utils.logger import get_root_logger
+from pointcept.utils.cache import shared_dict
+from .builder import DATASETS, build_dataset
+from .transform import Compose, TRANSFORMS
+
+
+@DATASETS.register_module()
+class DefaultDataset(Dataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/dataset",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+    ):
+        super(DefaultDataset, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.loop = loop if not test_mode else 1  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+
+        if test_mode:
+            self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize) if self.test_cfg.voxelize is not None else None
+            self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop is not None else None
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data = torch.load(self.data_list[idx % len(self.data_list)])
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        if "semantic_gt" in data.keys():
+            segment = data["semantic_gt"].reshape([-1])
+        else:
+            segment = np.ones(coord.shape[0]) * -1
+        data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        result_dict = dict(segment=data_dict.pop("segment"), name=self.get_data_name(idx))
+        if "origin_segment" in data_dict:
+            assert "inverse" in data_dict
+            result_dict["origin_segment"] = data_dict.pop("origin_segment")
+            result_dict["inverse"] = data_dict.pop("inverse")
+
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        fragment_list = []
+        for data in data_dict_list:
+            if self.test_voxelize is not None:
+                data_part_list = self.test_voxelize(data)
+            else:
+                data["index"] = np.arange(data["coord"].shape[0])
+                data_part_list = [data]
+            for data_part in data_part_list:
+                if self.test_crop is not None:
+                    data_part = self.test_crop(data_part)
+                else:
+                    data_part = [data_part]
+                fragment_list += data_part
+
+        for i in range(len(fragment_list)):
+            fragment_list[i] = self.post_transform(fragment_list[i])
+        result_dict["fragment_list"] = fragment_list
+        return result_dict
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class DefaultDatasetV2(Dataset):
+    VALID_ASSETS = [
+        "coord",
+        "color",
+        "normal",
+        "strength",
+        "segment",
+        "instance",
+        "pose",
+    ]
+
+    def __init__(
+        self,
+        split="train",
+        data_root="data/dataset",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        cache=False,
+        ignore_index=-1,
+        loop=1,
+    ):
+        super(DefaultDatasetV2, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.cache = cache
+        self.ignore_index = ignore_index
+        self.loop = loop if not test_mode else 1  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+
+        if test_mode:
+            self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+            self.test_crop = TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info("Totally {} x {} samples in {} set.".format(len(self.data_list), self.loop, split))
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(os.path.join(self.data_root, self.split, "*"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(os.path.join(self.data_root, split, "*"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+        name = self.get_data_name(idx)
+        if self.cache:
+            cache_name = f"pointcept-{name}"
+            return shared_dict(cache_name)
+
+        data_dict = {}
+        assets = os.listdir(data_path)
+        for asset in assets:
+            if not asset.endswith(".npy"):
+                continue
+            if asset[:-4] not in self.VALID_ASSETS:
+                continue
+            data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+        data_dict["name"] = name
+
+        if "coord" in data_dict.keys():
+            data_dict["coord"] = data_dict["coord"].astype(np.float32)
+
+        if "color" in data_dict.keys():
+            data_dict["color"] = data_dict["color"].astype(np.float32)
+
+        if "normal" in data_dict.keys():
+            data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+        if "segment" in data_dict.keys():
+            data_dict["segment"] = data_dict["segment"].reshape([-1]).astype(np.int32)
+        else:
+            data_dict["segment"] = np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+
+        if "instance" in data_dict.keys():
+            data_dict["instance"] = data_dict["instance"].reshape([-1]).astype(np.int32)
+        else:
+            data_dict["instance"] = np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+        return data_dict
+
+    def get_data_name(self, idx):
+        return os.path.basename(self.data_list[idx % len(self.data_list)])
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        result_dict = dict(segment=data_dict.pop("segment"), name=data_dict.pop("name"))
+        if "origin_segment" in data_dict:
+            assert "inverse" in data_dict
+            result_dict["origin_segment"] = data_dict.pop("origin_segment")
+            result_dict["inverse"] = data_dict.pop("inverse")
+
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        fragment_list = []
+        for data in data_dict_list:
+            if self.test_voxelize is not None:
+                data_part_list = self.test_voxelize(data)
+            else:
+                data["index"] = np.arange(data["coord"].shape[0])
+                data_part_list = [data]
+            for data_part in data_part_list:
+                if self.test_crop is not None:
+                    data_part = self.test_crop(data_part)
+                else:
+                    data_part = [data_part]
+                fragment_list += data_part
+
+        for i in range(len(fragment_list)):
+            fragment_list[i] = self.post_transform(fragment_list[i])
+        result_dict["fragment_list"] = fragment_list
+        return result_dict
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ConcatDataset(Dataset):
+    def __init__(self, datasets, loop=1):
+        super(ConcatDataset, self).__init__()
+        self.datasets = [build_dataset(dataset) for dataset in datasets]
+        self.loop = loop
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info("Totally {} x {} samples in the concat set.".format(len(self.data_list), self.loop))
+
+    def get_data_list(self):
+        data_list = []
+        for i in range(len(self.datasets)):
+            data_list.extend(zip(np.ones(len(self.datasets[i])) * i, np.arange(len(self.datasets[i]))))
+        return data_list
+
+    def get_data(self, idx):
+        dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+        return self.datasets[dataset_idx][data_idx]
+
+    def get_data_name(self, idx):
+        dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+        return self.datasets[dataset_idx].get_data_name(data_idx)
+
+    def __getitem__(self, idx):
+        return self.get_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/modelnet.py

@@ -0,0 +1,150 @@
+"""
+ModelNet40 Dataset
+
+get sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape)
+at "https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip"
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import pointops
+import torch
+from torch.utils.data import Dataset
+from copy import deepcopy
+
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ModelNetDataset(Dataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/modelnet40",
+        class_names=None,
+        transform=None,
+        num_points=8192,
+        uniform_sampling=True,
+        save_record=True,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+    ):
+        super().__init__()
+        self.data_root = data_root
+        self.class_names = dict(zip(class_names, range(len(class_names))))
+        self.split = split
+        self.num_point = num_points
+        self.uniform_sampling = uniform_sampling
+        self.transform = Compose(transform)
+        self.loop = (
+            loop if not test_mode else 1
+        )  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+        if test_mode:
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_list), self.loop, split
+            )
+        )
+
+        # check, prepare record
+        record_name = f"modelnet40_{self.split}"
+        if num_points is not None:
+            record_name += f"_{num_points}points"
+            if uniform_sampling:
+                record_name += "_uniform"
+        record_path = os.path.join(self.data_root, f"{record_name}.pth")
+        if os.path.isfile(record_path):
+            logger.info(f"Loading record: {record_name} ...")
+            self.data = torch.load(record_path)
+        else:
+            logger.info(f"Preparing record: {record_name} ...")
+            self.data = {}
+            for idx in range(len(self.data_list)):
+                data_name = self.data_list[idx]
+                logger.info(f"Parsing data [{idx}/{len(self.data_list)}]: {data_name}")
+                self.data[data_name] = self.get_data(idx)
+            if save_record:
+                torch.save(self.data, record_path)
+
+    def get_data(self, idx):
+        data_idx = idx % len(self.data_list)
+        data_name = self.data_list[data_idx]
+        if data_name in self.data.keys():
+            return self.data[data_name]
+        else:
+            data_shape = "_".join(data_name.split("_")[0:-1])
+            data_path = os.path.join(
+                self.data_root, data_shape, self.data_list[data_idx] + ".txt"
+            )
+            data = np.loadtxt(data_path, delimiter=",").astype(np.float32)
+            if self.num_point is not None:
+                if self.uniform_sampling:
+                    with torch.no_grad():
+                        mask = pointops.farthest_point_sampling(
+                            torch.tensor(data).float().cuda(),
+                            torch.tensor([len(data)]).long().cuda(),
+                            torch.tensor([self.num_point]).long().cuda(),
+                        )
+                    data = data[mask.cpu()]
+                else:
+                    data = data[: self.num_point]
+            coord, normal = data[:, 0:3], data[:, 3:6]
+            category = np.array([self.class_names[data_shape]])
+            return dict(coord=coord, normal=normal, category=category)
+
+    def get_data_list(self):
+        assert isinstance(self.split, str)
+        split_path = os.path.join(
+            self.data_root, "modelnet40_{}.txt".format(self.split)
+        )
+        data_list = np.loadtxt(split_path, dtype="str")
+        return data_list
+
+    def get_data_name(self, idx):
+        data_idx = idx % len(self.data_list)
+        return self.data_list[data_idx]
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop
+
+    def prepare_train_data(self, idx):
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        assert idx < len(self.data_list)
+        data_dict = self.get_data(idx)
+        category = data_dict.pop("category")
+        data_dict = self.transform(data_dict)
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+        for i in range(len(data_dict_list)):
+            data_dict_list[i] = self.post_transform(data_dict_list[i])
+        data_dict = dict(
+            voting_list=data_dict_list,
+            category=category,
+            name=self.get_data_name(idx),
+        )
+        return data_dict

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/nuscenes.py

@@ -0,0 +1,120 @@
+"""
+nuScenes Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Zheng Zhang
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+from collections.abc import Sequence
+import pickle
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class NuScenesDataset(DefaultDataset):
+    def __init__(self, sweeps=10, ignore_index=-1, **kwargs):
+        self.sweeps = sweeps
+        self.ignore_index = ignore_index
+        self.learning_map = self.get_learning_map(ignore_index)
+        super().__init__(ignore_index=ignore_index, **kwargs)
+
+    def get_info_path(self, split):
+        assert split in ["train", "val", "test"]
+        if split == "train":
+            return os.path.join(
+                self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_train.pkl"
+            )
+        elif split == "val":
+            return os.path.join(
+                self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_val.pkl"
+            )
+        elif split == "test":
+            return os.path.join(
+                self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_test.pkl"
+            )
+        else:
+            raise NotImplementedError
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            info_paths = [self.get_info_path(self.split)]
+        elif isinstance(self.split, Sequence):
+            info_paths = [self.get_info_path(s) for s in self.split]
+        else:
+            raise NotImplementedError
+        data_list = []
+        for info_path in info_paths:
+            with open(info_path, "rb") as f:
+                info = pickle.load(f)
+                data_list.extend(info)
+        return data_list
+
+    def get_data(self, idx):
+        data = self.data_list[idx % len(self.data_list)]
+        lidar_path = os.path.join(self.data_root, "raw", data["lidar_path"])
+        points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1).reshape(
+            [-1, 5]
+        )
+        coord = points[:, :3]
+        strength = points[:, 3].reshape([-1, 1]) / 255  # scale strength to [0, 1]
+
+        if "gt_segment_path" in data.keys():
+            gt_segment_path = os.path.join(
+                self.data_root, "raw", data["gt_segment_path"]
+            )
+            segment = np.fromfile(
+                str(gt_segment_path), dtype=np.uint8, count=-1
+            ).reshape([-1])
+            segment = np.vectorize(self.learning_map.__getitem__)(segment).astype(
+                np.int64
+            )
+        else:
+            segment = np.ones((points.shape[0],), dtype=np.int64) * self.ignore_index
+        data_dict = dict(coord=coord, strength=strength, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        # return data name for lidar seg, optimize the code when need to support detection
+        return self.data_list[idx % len(self.data_list)]["lidar_token"]
+
+    @staticmethod
+    def get_learning_map(ignore_index):
+        learning_map = {
+            0: ignore_index,
+            1: ignore_index,
+            2: 6,
+            3: 6,
+            4: 6,
+            5: ignore_index,
+            6: 6,
+            7: ignore_index,
+            8: ignore_index,
+            9: 0,
+            10: ignore_index,
+            11: ignore_index,
+            12: 7,
+            13: ignore_index,
+            14: 1,
+            15: 2,
+            16: 2,
+            17: 3,
+            18: 4,
+            19: ignore_index,
+            20: ignore_index,
+            21: 5,
+            22: 8,
+            23: 9,
+            24: 10,
+            25: 11,
+            26: 12,
+            27: 13,
+            28: 14,
+            29: ignore_index,
+            30: 15,
+            31: ignore_index,
+        }
+        return learning_map

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/alc/preprocess_arkitscenes_labelmaker_consensus.py

@@ -0,0 +1,375 @@
+import warnings
+
+import torch
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import argparse
+import glob
+import json
+import multiprocessing as mp
+import os
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import plyfile
+from labelmaker import label_mappings
+from labelmaker.label_data import get_wordnet
+from labelmaker.scannet_200_labels import VALID_CLASS_IDS_200
+from tqdm import tqdm
+
+IGNORE_INDEX = -1
+
+
+def get_wordnet_to_scannet200_mapping():
+    table = pd.read_csv(Path(os.path.dirname(os.path.realpath(label_mappings.__file__))) / "mappings" / "label_mapping.csv")
+    wordnet = get_wordnet()
+    wordnet_keys = [x["name"] for x in wordnet]
+    mapping = {}
+    for row in table.index:
+        if table["wnsynsetkey"][row] not in wordnet_keys:
+            continue
+        scannet_id = table.loc[row, "id"]
+        wordnet199_id = next(x for x in wordnet if x["name"] == table["wnsynsetkey"][row])["id"]
+
+        if scannet_id in VALID_CLASS_IDS_200:
+            mapping.setdefault(wordnet199_id, set()).add(scannet_id)
+
+    wn199_size = np.array([x["id"] for x in wordnet]).max() + 1
+    mapping_array = np.zeros(shape=(wn199_size,), dtype=np.uint16)
+    for wordnet199_id in mapping.keys():
+        mapping_array[wordnet199_id] = min(mapping[wordnet199_id])
+
+    return mapping_array
+
+
+def get_wordnet_compact_mapping():
+    wordnet_info = get_wordnet()[1:]
+    wordnet_info = sorted(wordnet_info, key=lambda x: x["id"])
+
+    class2id = np.array([item["id"] for item in wordnet_info])
+    id2class = np.array([IGNORE_INDEX] * (class2id.max() + 1))
+    for class_, id_ in enumerate(class2id):
+        id2class[id_] = class_
+
+    return class2id, id2class
+
+
+def get_scannet200_compact_mapping():
+    class2id = np.array(VALID_CLASS_IDS_200)
+    id2class = np.array([IGNORE_INDEX] * (class2id.max() + 1))
+    for class_, id_ in enumerate(VALID_CLASS_IDS_200):
+        id2class[id_] = class_
+
+    return class2id, id2class
+
+
+def get_wordnet_names():
+    wordnet_info = get_wordnet()[1:]
+    wordnet_info = sorted(wordnet_info, key=lambda x: x["id"])
+
+    names = [item["name"].split(".")[0].replace("_", " ") for item in wordnet_info]
+
+    return names
+
+
+def read_plypcd(filepath):
+    """Read ply file and return it as numpy array. Returns None if emtpy."""
+
+    with open(filepath, "rb") as f:
+        plydata = plyfile.PlyData.read(f)
+    if plydata.elements:
+        data = plydata.elements[0].data
+        coords = np.array([data["x"], data["y"], data["z"]], dtype=np.float32).T
+
+        colors = None
+        if ({"red", "green", "blue"} - set(data.dtype.names)) == set():
+            colors = np.array([data["red"], data["green"], data["blue"]], dtype=np.uint8).T
+
+        normals = None
+        if ({"nx", "ny", "nz"} - set(data.dtype.names)) == set():
+            normals = np.array([data["nx"], data["ny"], data["nz"]], dtype=np.float32).T
+
+        return coords, colors, normals
+
+
+def handle_process(
+    scene_dir: str,
+    output_path: str,
+    label_mapping,
+    wn199_id2class,
+    scannet200_id2class,
+):
+    scene_dir = Path(scene_dir)
+
+    print(f"Processing: {scene_dir.name} in {scene_dir.parent.name}")
+
+    coords, colors, normals = read_plypcd(str(scene_dir / "pcd_downsampled.ply"))
+    save_dict = dict(
+        coord=coords,
+        color=colors,
+        scene_id=scene_dir.name,
+        normal=normals,
+    )
+
+    label_file = scene_dir / "labels_downsampled.txt"
+    wordnet_label = np.loadtxt(str(label_file), dtype=np.uint8).reshape(-1, 1)
+    scannet200_label = label_mapping[wordnet_label]
+    save_dict["semantic_pseudo_gt_wn199"] = wn199_id2class[wordnet_label]
+    save_dict["semantic_pseudo_gt_scannet200"] = scannet200_id2class[scannet200_label]
+
+    torch.save(save_dict, output_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    config = parser.parse_args()
+
+    # Create output directories
+    train_output_dir = os.path.join(config.output_root, "train")
+    os.makedirs(train_output_dir, exist_ok=True)
+    val_output_dir = os.path.join(config.output_root, "val")
+    os.makedirs(val_output_dir, exist_ok=True)
+
+    # Load label map
+    wn_scannet200_label_mapping = get_wordnet_to_scannet200_mapping()
+    _, wn199_id2class = get_wordnet_compact_mapping()
+    _, scannet200_id2class = get_scannet200_compact_mapping()
+
+    scene_dirs = []
+    output_paths = []
+
+    # Load train/val splits
+    train_folder = Path(config.dataset_root) / "Training"
+    train_scene_names = os.listdir(str(train_folder))
+    for scene in tqdm(train_scene_names):
+        file_path = train_folder / scene / "pcd_downsampled.ply"
+        if file_path.exists() and os.path.getsize(str(file_path)) <= 50 * 1024 * 1024:
+            scene_dirs.append(str(train_folder / scene))
+            output_paths.append(str(Path(config.output_root) / "train" / f"{scene}.pth"))
+
+    val_folder = Path(config.dataset_root) / "Validation"
+    val_scene_names = os.listdir(str(val_folder))
+    for scene in tqdm(val_scene_names):
+        file_path = val_folder / scene / "pcd_downsampled.ply"
+        if file_path.exists() and os.path.getsize(str(file_path)) <= 50 * 1024 * 1024:
+            scene_dirs.append(str(val_folder / scene))
+            output_paths.append(str(Path(config.output_root) / "val" / f"{scene}.pth"))
+
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    print(f"Using {mp.cpu_count()} cores")
+    # pool = ProcessPoolExecutor(max_workers=1)
+    _ = list(
+        pool.map(
+            handle_process,
+            scene_dirs,
+            output_paths,
+            repeat(wn_scannet200_label_mapping),
+            repeat(wn199_id2class),
+            repeat(scannet200_id2class),
+        )
+    )
+
+
+WORDNET_NAMES = (
+    "wall",
+    "chair",
+    "book",
+    "cabinet",
+    "door",
+    "floor",
+    "ashcan",
+    "table",
+    "window",
+    "bookshelf",
+    "display",
+    "cushion",
+    "box",
+    "picture",
+    "ceiling",
+    "doorframe",
+    "desk",
+    "swivel chair",
+    "towel",
+    "sofa",
+    "sink",
+    "backpack",
+    "lamp",
+    "chest of drawers",
+    "apparel",
+    "armchair",
+    "bed",
+    "curtain",
+    "mirror",
+    "plant",
+    "radiator",
+    "toilet tissue",
+    "shoe",
+    "bag",
+    "bottle",
+    "countertop",
+    "coffee table",
+    "toilet",
+    "computer keyboard",
+    "fridge",
+    "stool",
+    "computer",
+    "mug",
+    "telephone",
+    "light",
+    "jacket",
+    "bathtub",
+    "shower curtain",
+    "microwave",
+    "footstool",
+    "baggage",
+    "laptop",
+    "printer",
+    "shower stall",
+    "soap dispenser",
+    "stove",
+    "fan",
+    "paper",
+    "stand",
+    "bench",
+    "wardrobe",
+    "blanket",
+    "booth",
+    "duplicator",
+    "bar",
+    "soap dish",
+    "switch",
+    "coffee maker",
+    "decoration",
+    "range hood",
+    "blackboard",
+    "clock",
+    "railing",
+    "mat",
+    "seat",
+    "bannister",
+    "container",
+    "mouse",
+    "person",
+    "stairway",
+    "basket",
+    "dumbbell",
+    "column",
+    "bucket",
+    "windowsill",
+    "signboard",
+    "dishwasher",
+    "loudspeaker",
+    "washer",
+    "paper towel",
+    "clothes hamper",
+    "piano",
+    "sack",
+    "handcart",
+    "blind",
+    "dish rack",
+    "mailbox",
+    "bag",
+    "bicycle",
+    "ladder",
+    "rack",
+    "tray",
+    "toaster",
+    "paper cutter",
+    "plunger",
+    "dryer",
+    "guitar",
+    "fire extinguisher",
+    "pitcher",
+    "pipe",
+    "plate",
+    "vacuum",
+    "bowl",
+    "hat",
+    "rod",
+    "water cooler",
+    "kettle",
+    "oven",
+    "scale",
+    "broom",
+    "hand blower",
+    "coatrack",
+    "teddy",
+    "alarm clock",
+    "ironing board",
+    "fire alarm",
+    "machine",
+    "music stand",
+    "fireplace",
+    "furniture",
+    "vase",
+    "vent",
+    "candle",
+    "crate",
+    "dustpan",
+    "earphone",
+    "jar",
+    "projector",
+    "gat",
+    "step",
+    "step stool",
+    "vending machine",
+    "coat",
+    "coat hanger",
+    "drinking fountain",
+    "hamper",
+    "thermostat",
+    "banner",
+    "iron",
+    "soap",
+    "chopping board",
+    "kitchen island",
+    "shirt",
+    "sleeping bag",
+    "tire",
+    "toothbrush",
+    "bathrobe",
+    "faucet",
+    "slipper",
+    "thermos",
+    "tripod",
+    "dispenser",
+    "heater",
+    "pool table",
+    "remote control",
+    "stapler",
+    "treadmill",
+    "beanbag",
+    "dartboard",
+    "metronome",
+    "rope",
+    "sewing machine",
+    "shredder",
+    "toolbox",
+    "water heater",
+    "brush",
+    "control",
+    "dais",
+    "dollhouse",
+    "envelope",
+    "food",
+    "frying pan",
+    "helmet",
+    "tennis racket",
+    "umbrella",
+)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py

@@ -0,0 +1,87 @@
+"""
+Preprocessing ArkitScenes
+"""
+
+import os
+import argparse
+import glob
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+import torch
+
+
+def read_plymesh(filepath):
+    """Read ply file and return it as numpy array. Returns None if emtpy."""
+    with open(filepath, "rb") as f:
+        plydata = plyfile.PlyData.read(f)
+    if plydata.elements:
+        vertices = pd.DataFrame(plydata["vertex"].data).values
+        faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+        return vertices, faces
+
+
+def face_normal(vertex, face):
+    v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+    v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+    vec = np.cross(v01, v02)
+    length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+    nf = vec / length
+    area = length * 0.5
+    return nf, area
+
+
+def vertex_normal(vertex, face):
+    nf, area = face_normal(vertex, face)
+    nf = nf * area
+
+    nv = np.zeros_like(vertex)
+    for i in range(face.shape[0]):
+        nv[face[i]] += nf[i]
+
+    length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+    nv = nv / length
+    return nv
+
+
+def parse_scene(scene_path, output_dir):
+    print(f"Parsing scene {scene_path}")
+    split = os.path.basename(os.path.dirname(os.path.dirname(scene_path)))
+    scene_id = os.path.basename(os.path.dirname(scene_path))
+    vertices, faces = read_plymesh(scene_path)
+    coords = vertices[:, :3]
+    colors = vertices[:, 3:6]
+    data_dict = dict(coord=coords, color=colors, scene_id=scene_id)
+    data_dict["normal"] = vertex_normal(coords, faces)
+    torch.save(data_dict, os.path.join(output_dir, split, f"{scene_id}.pth"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    opt = parser.parse_args()
+    # Create output directories
+    train_output_dir = os.path.join(opt.output_root, "Training")
+    os.makedirs(train_output_dir, exist_ok=True)
+    val_output_dir = os.path.join(opt.output_root, "Validation")
+    os.makedirs(val_output_dir, exist_ok=True)
+    # Load scene paths
+    scene_paths = sorted(glob.glob(opt.dataset_root + "/3dod/*/*/*_mesh.ply"))
+    # Preprocess data.
+    pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    # pool = ProcessPoolExecutor(max_workers=1)
+    print("Processing scenes...")
+    _ = list(pool.map(parse_scene, scene_paths, repeat(opt.output_root)))

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py

@@ -0,0 +1,607 @@
+"""
+Preprocessing Script for nuScenes Informantion
+modified from OpenPCDet (https://github.com/open-mmlab/OpenPCDet)
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from pathlib import Path
+import numpy as np
+import argparse
+import tqdm
+import pickle
+from functools import reduce
+from pyquaternion import Quaternion
+from nuscenes.nuscenes import NuScenes
+from nuscenes.utils import splits
+from nuscenes.utils.geometry_utils import transform_matrix
+
+
+map_name_from_general_to_detection = {
+    "human.pedestrian.adult": "pedestrian",
+    "human.pedestrian.child": "pedestrian",
+    "human.pedestrian.wheelchair": "ignore",
+    "human.pedestrian.stroller": "ignore",
+    "human.pedestrian.personal_mobility": "ignore",
+    "human.pedestrian.police_officer": "pedestrian",
+    "human.pedestrian.construction_worker": "pedestrian",
+    "animal": "ignore",
+    "vehicle.car": "car",
+    "vehicle.motorcycle": "motorcycle",
+    "vehicle.bicycle": "bicycle",
+    "vehicle.bus.bendy": "bus",
+    "vehicle.bus.rigid": "bus",
+    "vehicle.truck": "truck",
+    "vehicle.construction": "construction_vehicle",
+    "vehicle.emergency.ambulance": "ignore",
+    "vehicle.emergency.police": "ignore",
+    "vehicle.trailer": "trailer",
+    "movable_object.barrier": "barrier",
+    "movable_object.trafficcone": "traffic_cone",
+    "movable_object.pushable_pullable": "ignore",
+    "movable_object.debris": "ignore",
+    "static_object.bicycle_rack": "ignore",
+}
+
+
+cls_attr_dist = {
+    "barrier": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "bicycle": {
+        "cycle.with_rider": 2791,
+        "cycle.without_rider": 8946,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "bus": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 9092,
+        "vehicle.parked": 3294,
+        "vehicle.stopped": 3881,
+    },
+    "car": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 114304,
+        "vehicle.parked": 330133,
+        "vehicle.stopped": 46898,
+    },
+    "construction_vehicle": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 882,
+        "vehicle.parked": 11549,
+        "vehicle.stopped": 2102,
+    },
+    "ignore": {
+        "cycle.with_rider": 307,
+        "cycle.without_rider": 73,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 165,
+        "vehicle.parked": 400,
+        "vehicle.stopped": 102,
+    },
+    "motorcycle": {
+        "cycle.with_rider": 4233,
+        "cycle.without_rider": 8326,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "pedestrian": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 157444,
+        "pedestrian.sitting_lying_down": 13939,
+        "pedestrian.standing": 46530,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "traffic_cone": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "trailer": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 3421,
+        "vehicle.parked": 19224,
+        "vehicle.stopped": 1895,
+    },
+    "truck": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 21339,
+        "vehicle.parked": 55626,
+        "vehicle.stopped": 11097,
+    },
+}
+
+
+def get_available_scenes(nusc):
+    available_scenes = []
+    for scene in nusc.scene:
+        scene_token = scene["token"]
+        scene_rec = nusc.get("scene", scene_token)
+        sample_rec = nusc.get("sample", scene_rec["first_sample_token"])
+        sd_rec = nusc.get("sample_data", sample_rec["data"]["LIDAR_TOP"])
+        has_more_frames = True
+        scene_not_exist = False
+        while has_more_frames:
+            lidar_path, boxes, _ = nusc.get_sample_data(sd_rec["token"])
+            if not Path(lidar_path).exists():
+                scene_not_exist = True
+                break
+            else:
+                break
+        if scene_not_exist:
+            continue
+        available_scenes.append(scene)
+    return available_scenes
+
+
+def get_sample_data(nusc, sample_data_token, selected_anntokens=None):
+    """
+    Returns the data path as well as all annotations related to that sample_data.
+    Note that the boxes are transformed into the current sensor"s coordinate frame.
+    Args:
+        nusc:
+        sample_data_token: Sample_data token.
+        selected_anntokens: If provided only return the selected annotation.
+
+    Returns:
+
+    """
+    # Retrieve sensor & pose records
+    sd_record = nusc.get("sample_data", sample_data_token)
+    cs_record = nusc.get("calibrated_sensor", sd_record["calibrated_sensor_token"])
+    sensor_record = nusc.get("sensor", cs_record["sensor_token"])
+    pose_record = nusc.get("ego_pose", sd_record["ego_pose_token"])
+
+    data_path = nusc.get_sample_data_path(sample_data_token)
+
+    if sensor_record["modality"] == "camera":
+        cam_intrinsic = np.array(cs_record["camera_intrinsic"])
+    else:
+        cam_intrinsic = None
+
+    # Retrieve all sample annotations and map to sensor coordinate system.
+    if selected_anntokens is not None:
+        boxes = list(map(nusc.get_box, selected_anntokens))
+    else:
+        boxes = nusc.get_boxes(sample_data_token)
+
+    # Make list of Box objects including coord system transforms.
+    box_list = []
+    for box in boxes:
+        box.velocity = nusc.box_velocity(box.token)
+        # Move box to ego vehicle coord system
+        box.translate(-np.array(pose_record["translation"]))
+        box.rotate(Quaternion(pose_record["rotation"]).inverse)
+
+        #  Move box to sensor coord system
+        box.translate(-np.array(cs_record["translation"]))
+        box.rotate(Quaternion(cs_record["rotation"]).inverse)
+
+        box_list.append(box)
+
+    return data_path, box_list, cam_intrinsic
+
+
+def quaternion_yaw(q: Quaternion) -> float:
+    """
+    Calculate the yaw angle from a quaternion.
+    Note that this only works for a quaternion that represents a box in lidar or global coordinate frame.
+    It does not work for a box in the camera frame.
+    :param q: Quaternion of interest.
+    :return: Yaw angle in radians.
+    """
+
+    # Project into xy plane.
+    v = np.dot(q.rotation_matrix, np.array([1, 0, 0]))
+
+    # Measure yaw using arctan.
+    yaw = np.arctan2(v[1], v[0])
+
+    return yaw
+
+
+def obtain_sensor2top(
+    nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type="lidar"
+):
+    """Obtain the info with RT matric from general sensor to Top LiDAR.
+
+    Args:
+        nusc (class): Dataset class in the nuScenes dataset.
+        sensor_token (str): Sample data token corresponding to the
+            specific sensor type.
+        l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3).
+        l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego
+            in shape (3, 3).
+        e2g_t (np.ndarray): Translation from ego to global in shape (1, 3).
+        e2g_r_mat (np.ndarray): Rotation matrix from ego to global
+            in shape (3, 3).
+        sensor_type (str): Sensor to calibrate. Default: "lidar".
+
+    Returns:
+        sweep (dict): Sweep information after transformation.
+    """
+    sd_rec = nusc.get("sample_data", sensor_token)
+    cs_record = nusc.get("calibrated_sensor", sd_rec["calibrated_sensor_token"])
+    pose_record = nusc.get("ego_pose", sd_rec["ego_pose_token"])
+    data_path = str(nusc.get_sample_data_path(sd_rec["token"]))
+    # if os.getcwd() in data_path:  # path from lyftdataset is absolute path
+    #     data_path = data_path.split(f"{os.getcwd()}/")[-1]  # relative path
+    sweep = {
+        "data_path": data_path,
+        "type": sensor_type,
+        "sample_data_token": sd_rec["token"],
+        "sensor2ego_translation": cs_record["translation"],
+        "sensor2ego_rotation": cs_record["rotation"],
+        "ego2global_translation": pose_record["translation"],
+        "ego2global_rotation": pose_record["rotation"],
+        "timestamp": sd_rec["timestamp"],
+    }
+    l2e_r_s = sweep["sensor2ego_rotation"]
+    l2e_t_s = sweep["sensor2ego_translation"]
+    e2g_r_s = sweep["ego2global_rotation"]
+    e2g_t_s = sweep["ego2global_translation"]
+
+    # obtain the RT from sensor to Top LiDAR
+    # sweep->ego->global->ego'->lidar
+    l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
+    e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
+    R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
+        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+    )
+    T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
+        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+    )
+    T -= (
+        e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+        + l2e_t @ np.linalg.inv(l2e_r_mat).T
+    ).squeeze(0)
+    sweep["sensor2lidar_rotation"] = R.T  # points @ R.T + T
+    sweep["sensor2lidar_translation"] = T
+    return sweep
+
+
+def fill_trainval_infos(
+    data_path, nusc, train_scenes, test=False, max_sweeps=10, with_camera=False
+):
+    train_nusc_infos = []
+    val_nusc_infos = []
+    progress_bar = tqdm.tqdm(
+        total=len(nusc.sample), desc="create_info", dynamic_ncols=True
+    )
+
+    ref_chan = "LIDAR_TOP"  # The radar channel from which we track back n sweeps to aggregate the point cloud.
+    chan = "LIDAR_TOP"  # The reference channel of the current sample_rec that the point clouds are mapped to.
+
+    for index, sample in enumerate(nusc.sample):
+        progress_bar.update()
+
+        ref_sd_token = sample["data"][ref_chan]
+        ref_sd_rec = nusc.get("sample_data", ref_sd_token)
+        ref_cs_rec = nusc.get(
+            "calibrated_sensor", ref_sd_rec["calibrated_sensor_token"]
+        )
+        ref_pose_rec = nusc.get("ego_pose", ref_sd_rec["ego_pose_token"])
+        ref_time = 1e-6 * ref_sd_rec["timestamp"]
+
+        ref_lidar_path, ref_boxes, _ = get_sample_data(nusc, ref_sd_token)
+
+        ref_cam_front_token = sample["data"]["CAM_FRONT"]
+        ref_cam_path, _, ref_cam_intrinsic = nusc.get_sample_data(ref_cam_front_token)
+
+        # Homogeneous transform from ego car frame to reference frame
+        ref_from_car = transform_matrix(
+            ref_cs_rec["translation"], Quaternion(ref_cs_rec["rotation"]), inverse=True
+        )
+
+        # Homogeneous transformation matrix from global to _current_ ego car frame
+        car_from_global = transform_matrix(
+            ref_pose_rec["translation"],
+            Quaternion(ref_pose_rec["rotation"]),
+            inverse=True,
+        )
+        info = {
+            "lidar_path": Path(ref_lidar_path).relative_to(data_path).__str__(),
+            "lidar_token": ref_sd_token,
+            "cam_front_path": Path(ref_cam_path).relative_to(data_path).__str__(),
+            "cam_intrinsic": ref_cam_intrinsic,
+            "token": sample["token"],
+            "sweeps": [],
+            "ref_from_car": ref_from_car,
+            "car_from_global": car_from_global,
+            "timestamp": ref_time,
+        }
+        if with_camera:
+            info["cams"] = dict()
+            l2e_r = ref_cs_rec["rotation"]
+            l2e_t = (ref_cs_rec["translation"],)
+            e2g_r = ref_pose_rec["rotation"]
+            e2g_t = ref_pose_rec["translation"]
+            l2e_r_mat = Quaternion(l2e_r).rotation_matrix
+            e2g_r_mat = Quaternion(e2g_r).rotation_matrix
+
+            # obtain 6 image's information per frame
+            camera_types = [
+                "CAM_FRONT",
+                "CAM_FRONT_RIGHT",
+                "CAM_FRONT_LEFT",
+                "CAM_BACK",
+                "CAM_BACK_LEFT",
+                "CAM_BACK_RIGHT",
+            ]
+            for cam in camera_types:
+                cam_token = sample["data"][cam]
+                cam_path, _, camera_intrinsics = nusc.get_sample_data(cam_token)
+                cam_info = obtain_sensor2top(
+                    nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam
+                )
+                cam_info["data_path"] = (
+                    Path(cam_info["data_path"]).relative_to(data_path).__str__()
+                )
+                cam_info.update(camera_intrinsics=camera_intrinsics)
+                info["cams"].update({cam: cam_info})
+
+        sample_data_token = sample["data"][chan]
+        curr_sd_rec = nusc.get("sample_data", sample_data_token)
+        sweeps = []
+        while len(sweeps) < max_sweeps - 1:
+            if curr_sd_rec["prev"] == "":
+                if len(sweeps) == 0:
+                    sweep = {
+                        "lidar_path": Path(ref_lidar_path)
+                        .relative_to(data_path)
+                        .__str__(),
+                        "sample_data_token": curr_sd_rec["token"],
+                        "transform_matrix": None,
+                        "time_lag": curr_sd_rec["timestamp"] * 0,
+                    }
+                    sweeps.append(sweep)
+                else:
+                    sweeps.append(sweeps[-1])
+            else:
+                curr_sd_rec = nusc.get("sample_data", curr_sd_rec["prev"])
+
+                # Get past pose
+                current_pose_rec = nusc.get("ego_pose", curr_sd_rec["ego_pose_token"])
+                global_from_car = transform_matrix(
+                    current_pose_rec["translation"],
+                    Quaternion(current_pose_rec["rotation"]),
+                    inverse=False,
+                )
+
+                # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.
+                current_cs_rec = nusc.get(
+                    "calibrated_sensor", curr_sd_rec["calibrated_sensor_token"]
+                )
+                car_from_current = transform_matrix(
+                    current_cs_rec["translation"],
+                    Quaternion(current_cs_rec["rotation"]),
+                    inverse=False,
+                )
+
+                tm = reduce(
+                    np.dot,
+                    [ref_from_car, car_from_global, global_from_car, car_from_current],
+                )
+
+                lidar_path = nusc.get_sample_data_path(curr_sd_rec["token"])
+
+                time_lag = ref_time - 1e-6 * curr_sd_rec["timestamp"]
+
+                sweep = {
+                    "lidar_path": Path(lidar_path).relative_to(data_path).__str__(),
+                    "sample_data_token": curr_sd_rec["token"],
+                    "transform_matrix": tm,
+                    "global_from_car": global_from_car,
+                    "car_from_current": car_from_current,
+                    "time_lag": time_lag,
+                }
+                sweeps.append(sweep)
+
+        info["sweeps"] = sweeps
+
+        assert len(info["sweeps"]) == max_sweeps - 1, (
+            f"sweep {curr_sd_rec['token']} only has {len(info['sweeps'])} sweeps, "
+            f"you should duplicate to sweep num {max_sweeps - 1}"
+        )
+
+        if not test:
+            # processing gt bbox
+            annotations = [
+                nusc.get("sample_annotation", token) for token in sample["anns"]
+            ]
+
+            # the filtering gives 0.5~1 map improvement
+            num_lidar_pts = np.array([anno["num_lidar_pts"] for anno in annotations])
+            num_radar_pts = np.array([anno["num_radar_pts"] for anno in annotations])
+            mask = num_lidar_pts + num_radar_pts > 0
+
+            locs = np.array([b.center for b in ref_boxes]).reshape(-1, 3)
+            dims = np.array([b.wlh for b in ref_boxes]).reshape(-1, 3)[
+                :, [1, 0, 2]
+            ]  # wlh == > dxdydz (lwh)
+            velocity = np.array([b.velocity for b in ref_boxes]).reshape(-1, 3)
+            rots = np.array([quaternion_yaw(b.orientation) for b in ref_boxes]).reshape(
+                -1, 1
+            )
+            names = np.array([b.name for b in ref_boxes])
+            tokens = np.array([b.token for b in ref_boxes])
+            gt_boxes = np.concatenate([locs, dims, rots, velocity[:, :2]], axis=1)
+
+            assert len(annotations) == len(gt_boxes) == len(velocity)
+
+            info["gt_boxes"] = gt_boxes[mask, :]
+            info["gt_boxes_velocity"] = velocity[mask, :]
+            info["gt_names"] = np.array(
+                [map_name_from_general_to_detection[name] for name in names]
+            )[mask]
+            info["gt_boxes_token"] = tokens[mask]
+            info["num_lidar_pts"] = num_lidar_pts[mask]
+            info["num_radar_pts"] = num_radar_pts[mask]
+
+            # processing gt segment
+            segment_path = nusc.get("lidarseg", ref_sd_token)["filename"]
+            info["gt_segment_path"] = segment_path
+
+        if sample["scene_token"] in train_scenes:
+            train_nusc_infos.append(info)
+        else:
+            val_nusc_infos.append(info)
+
+    progress_bar.close()
+    return train_nusc_infos, val_nusc_infos
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root", required=True, help="Path to the nuScenes dataset."
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where processed information located.",
+    )
+    parser.add_argument(
+        "--max_sweeps", default=10, type=int, help="Max number of sweeps. Default: 10."
+    )
+    parser.add_argument(
+        "--with_camera",
+        action="store_true",
+        default=False,
+        help="Whether use camera or not.",
+    )
+    config = parser.parse_args()
+
+    print(f"Loading nuScenes tables for version v1.0-trainval...")
+    nusc_trainval = NuScenes(
+        version="v1.0-trainval", dataroot=config.dataset_root, verbose=False
+    )
+    available_scenes_trainval = get_available_scenes(nusc_trainval)
+    available_scene_names_trainval = [s["name"] for s in available_scenes_trainval]
+    print("total scene num:", len(nusc_trainval.scene))
+    print("exist scene num:", len(available_scenes_trainval))
+    assert len(available_scenes_trainval) == len(nusc_trainval.scene) == 850
+
+    print(f"Loading nuScenes tables for version v1.0-test...")
+    nusc_test = NuScenes(
+        version="v1.0-test", dataroot=config.dataset_root, verbose=False
+    )
+    available_scenes_test = get_available_scenes(nusc_test)
+    available_scene_names_test = [s["name"] for s in available_scenes_test]
+    print("total scene num:", len(nusc_test.scene))
+    print("exist scene num:", len(available_scenes_test))
+    assert len(available_scenes_test) == len(nusc_test.scene) == 150
+
+    train_scenes = splits.train
+    train_scenes = set(
+        [
+            available_scenes_trainval[available_scene_names_trainval.index(s)]["token"]
+            for s in train_scenes
+        ]
+    )
+    test_scenes = splits.test
+    test_scenes = set(
+        [
+            available_scenes_test[available_scene_names_test.index(s)]["token"]
+            for s in test_scenes
+        ]
+    )
+    print(f"Filling trainval information...")
+    train_nusc_infos, val_nusc_infos = fill_trainval_infos(
+        config.dataset_root,
+        nusc_trainval,
+        train_scenes,
+        test=False,
+        max_sweeps=config.max_sweeps,
+        with_camera=config.with_camera,
+    )
+    print(f"Filling test information...")
+    test_nusc_infos, _ = fill_trainval_infos(
+        config.dataset_root,
+        nusc_test,
+        test_scenes,
+        test=True,
+        max_sweeps=config.max_sweeps,
+        with_camera=config.with_camera,
+    )
+
+    print(f"Saving nuScenes information...")
+    os.makedirs(os.path.join(config.output_root, "info"), exist_ok=True)
+    print(
+        f"train sample: {len(train_nusc_infos)}, val sample: {len(val_nusc_infos)}, test sample: {len(test_nusc_infos)}"
+    )
+    with open(
+        os.path.join(
+            config.output_root,
+            "info",
+            f"nuscenes_infos_{config.max_sweeps}sweeps_train.pkl",
+        ),
+        "wb",
+    ) as f:
+        pickle.dump(train_nusc_infos, f)
+    with open(
+        os.path.join(
+            config.output_root,
+            "info",
+            f"nuscenes_infos_{config.max_sweeps}sweeps_val.pkl",
+        ),
+        "wb",
+    ) as f:
+        pickle.dump(val_nusc_infos, f)
+    with open(
+        os.path.join(
+            config.output_root,
+            "info",
+            f"nuscenes_infos_{config.max_sweeps}sweeps_test.pkl",
+        ),
+        "wb",
+    ) as f:
+        pickle.dump(test_nusc_infos, f)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py

@@ -0,0 +1,233 @@
+"""
+Preprocessing Script for S3DIS
+Parsing normal vectors has a large consumption of memory. Please reduce max_workers if memory is limited.
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import glob
+import numpy as np
+
+try:
+    import open3d
+except ImportError:
+    import warnings
+
+    warnings.warn("Please install open3d for parsing normal")
+
+try:
+    import trimesh
+except ImportError:
+    import warnings
+
+    warnings.warn("Please install trimesh for parsing normal")
+
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+area_mesh_dict = {}
+
+
+def parse_room(
+    room, angle, dataset_root, output_root, align_angle=True, parse_normal=False
+):
+    print("Parsing: {}".format(room))
+    classes = [
+        "ceiling",
+        "floor",
+        "wall",
+        "beam",
+        "column",
+        "window",
+        "door",
+        "table",
+        "chair",
+        "sofa",
+        "bookcase",
+        "board",
+        "clutter",
+    ]
+    class2label = {cls: i for i, cls in enumerate(classes)}
+    source_dir = os.path.join(dataset_root, room)
+    save_path = os.path.join(output_root, room)
+    os.makedirs(save_path, exist_ok=True)
+    object_path_list = sorted(glob.glob(os.path.join(source_dir, "Annotations/*.txt")))
+
+    room_coords = []
+    room_colors = []
+    room_normals = []
+    room_semantic_gt = []
+    room_instance_gt = []
+
+    for object_id, object_path in enumerate(object_path_list):
+        object_name = os.path.basename(object_path).split("_")[0]
+        obj = np.loadtxt(object_path)
+        coords = obj[:, :3]
+        colors = obj[:, 3:6]
+        # note: in some room there is 'stairs' class
+        class_name = object_name if object_name in classes else "clutter"
+        semantic_gt = np.repeat(class2label[class_name], coords.shape[0])
+        semantic_gt = semantic_gt.reshape([-1, 1])
+        instance_gt = np.repeat(object_id, coords.shape[0])
+        instance_gt = instance_gt.reshape([-1, 1])
+
+        room_coords.append(coords)
+        room_colors.append(colors)
+        room_semantic_gt.append(semantic_gt)
+        room_instance_gt.append(instance_gt)
+
+    room_coords = np.ascontiguousarray(np.vstack(room_coords))
+
+    if parse_normal:
+        x_min, z_max, y_min = np.min(room_coords, axis=0)
+        x_max, z_min, y_max = np.max(room_coords, axis=0)
+        z_max = -z_max
+        z_min = -z_min
+        max_bound = np.array([x_max, y_max, z_max]) + 0.1
+        min_bound = np.array([x_min, y_min, z_min]) - 0.1
+        bbox = open3d.geometry.AxisAlignedBoundingBox(
+            min_bound=min_bound, max_bound=max_bound
+        )
+        # crop room
+        room_mesh = (
+            area_mesh_dict[os.path.dirname(room)]
+            .crop(bbox)
+            .transform(
+                np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
+            )
+        )
+        vertices = np.array(room_mesh.vertices)
+        faces = np.array(room_mesh.triangles)
+        vertex_normals = np.array(room_mesh.vertex_normals)
+        room_mesh = trimesh.Trimesh(
+            vertices=vertices, faces=faces, vertex_normals=vertex_normals
+        )
+        (closest_points, distances, face_id) = room_mesh.nearest.on_surface(room_coords)
+        room_normals = room_mesh.face_normals[face_id]
+
+    if align_angle:
+        angle = (2 - angle / 180) * np.pi
+        rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+        rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+        room_center = (np.max(room_coords, axis=0) + np.min(room_coords, axis=0)) / 2
+        room_coords = (room_coords - room_center) @ np.transpose(rot_t) + room_center
+        if parse_normal:
+            room_normals = room_normals @ np.transpose(rot_t)
+
+    room_colors = np.ascontiguousarray(np.vstack(room_colors))
+    room_semantic_gt = np.ascontiguousarray(np.vstack(room_semantic_gt))
+    room_instance_gt = np.ascontiguousarray(np.vstack(room_instance_gt))
+    np.save(os.path.join(save_path, "coord.npy"), room_coords.astype(np.float32))
+    np.save(os.path.join(save_path, "color.npy"), room_colors.astype(np.uint8))
+    np.save(os.path.join(save_path, "segment.npy"), room_semantic_gt.astype(np.int16))
+    np.save(os.path.join(save_path, "instance.npy"), room_instance_gt.astype(np.int16))
+
+    if parse_normal:
+        np.save(os.path.join(save_path, "normal.npy"), room_normals.astype(np.float32))
+
+
+def main_process():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--splits",
+        required=True,
+        nargs="+",
+        choices=["Area_1", "Area_2", "Area_3", "Area_4", "Area_5", "Area_6"],
+        help="Splits need to process ([Area_1, Area_2, Area_3, Area_4, Area_5, Area_6]).",
+    )
+    parser.add_argument(
+        "--dataset_root", required=True, help="Path to Stanford3dDataset_v1.2 dataset"
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where area folders will be located",
+    )
+    parser.add_argument(
+        "--raw_root",
+        default=None,
+        help="Path to Stanford2d3dDataset_noXYZ dataset (optional)",
+    )
+    parser.add_argument(
+        "--align_angle", action="store_true", help="Whether align room angles"
+    )
+    parser.add_argument(
+        "--parse_normal", action="store_true", help="Whether process normal"
+    )
+    parser.add_argument(
+        "--num_workers", default=1, type=int, help="Num workers for preprocessing."
+    )
+    args = parser.parse_args()
+
+    if args.parse_normal:
+        assert args.raw_root is not None
+
+    room_list = []
+    angle_list = []
+
+    # Load room information
+    print("Loading room information ...")
+    for split in args.splits:
+        area_info = np.loadtxt(
+            os.path.join(
+                args.dataset_root,
+                split,
+                f"{split}_alignmentAngle.txt",
+            ),
+            dtype=str,
+        )
+        room_list += [os.path.join(split, room_info[0]) for room_info in area_info]
+        angle_list += [int(room_info[1]) for room_info in area_info]
+
+    if args.parse_normal:
+        # load raw mesh file to extract normal
+        print("Loading raw mesh file ...")
+        for split in args.splits:
+            if split != "Area_5":
+                mesh_dir = os.path.join(args.raw_root, split, "3d", "rgb.obj")
+                mesh = open3d.io.read_triangle_mesh(mesh_dir)
+                mesh.triangle_uvs.clear()
+            else:
+                mesh_a_dir = os.path.join(args.raw_root, f"{split}a", "3d", "rgb.obj")
+                mesh_b_dir = os.path.join(args.raw_root, f"{split}b", "3d", "rgb.obj")
+                mesh_a = open3d.io.read_triangle_mesh(mesh_a_dir)
+                mesh_a.triangle_uvs.clear()
+                mesh_b = open3d.io.read_triangle_mesh(mesh_b_dir)
+                mesh_b.triangle_uvs.clear()
+                mesh_b = mesh_b.transform(
+                    np.array(
+                        [
+                            [0, 0, -1, -4.09703582],
+                            [0, 1, 0, 0],
+                            [1, 0, 0, -6.22617759],
+                            [0, 0, 0, 1],
+                        ]
+                    )
+                )
+                mesh = mesh_a + mesh_b
+            area_mesh_dict[split] = mesh
+            print(f"{split} mesh is loaded")
+
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(
+        max_workers=args.num_workers
+    )  # peak 110G memory when parsing normal.
+    _ = list(
+        pool.map(
+            parse_room,
+            room_list,
+            angle_list,
+            repeat(args.dataset_root),
+            repeat(args.output_root),
+            repeat(args.align_angle),
+            repeat(args.parse_normal),
+        )
+    )
+
+
+if __name__ == "__main__":
+    main_process()

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/sampling_chunking_data.py

@@ -0,0 +1,149 @@
+"""
+Chunking Data
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+
+def chunking_scene(
+    name,
+    dataset_root,
+    split,
+    grid_size=None,
+    chunk_range=(6, 6),
+    chunk_stride=(3, 3),
+    chunk_minimum_size=10000,
+):
+    print(f"Chunking scene {name} in {split} split")
+    dataset_root = Path(dataset_root)
+    scene_path = dataset_root / split / name
+    assets = os.listdir(scene_path)
+    data_dict = dict()
+    for asset in assets:
+        if not asset.endswith(".npy"):
+            continue
+        data_dict[asset[:-4]] = np.load(scene_path / asset)
+    coord = data_dict["coord"] - data_dict["coord"].min(axis=0)
+
+    if grid_size is not None:
+        grid_coord = np.floor(coord / grid_size).astype(int)
+        _, idx = np.unique(grid_coord, axis=0, return_index=True)
+        coord = coord[idx]
+        for key in data_dict.keys():
+            data_dict[key] = data_dict[key][idx]
+
+    bev_range = coord.max(axis=0)[:2]
+    x, y = np.meshgrid(
+        np.arange(0, bev_range[0] + chunk_stride[0] - chunk_range[0], chunk_stride[0]),
+        np.arange(0, bev_range[0] + chunk_stride[0] - chunk_range[0], chunk_stride[0]),
+        indexing="ij",
+    )
+    chunks = np.concatenate([x.reshape([-1, 1]), y.reshape([-1, 1])], axis=-1)
+    chunk_idx = 0
+    for chunk in chunks:
+        mask = (
+            (coord[:, 0] >= chunk[0])
+            & (coord[:, 0] < chunk[0] + chunk_range[0])
+            & (coord[:, 1] >= chunk[1])
+            & (coord[:, 1] < chunk[1] + chunk_range[1])
+        )
+        if np.sum(mask) < chunk_minimum_size:
+            continue
+
+        chunk_data_name = f"{name}_{chunk_idx}"
+        if grid_size is not None:
+            chunk_split_name = (
+                f"{split}_"
+                f"grid{grid_size * 100:.0f}mm_"
+                f"chunk{chunk_range[0]}x{chunk_range[1]}_"
+                f"stride{chunk_stride[0]}x{chunk_stride[1]}"
+            )
+        else:
+            chunk_split_name = (
+                f"{split}_"
+                f"chunk{chunk_range[0]}x{chunk_range[1]}_"
+                f"stride{chunk_stride[0]}x{chunk_stride[1]}"
+            )
+
+        chunk_save_path = dataset_root / chunk_split_name / chunk_data_name
+        chunk_save_path.mkdir(parents=True, exist_ok=True)
+        for key in data_dict.keys():
+            np.save(chunk_save_path / f"{key}.npy", data_dict[key][mask])
+        chunk_idx += 1
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the Pointcept processed ScanNet++ dataset.",
+    )
+    parser.add_argument(
+        "--split",
+        required=True,
+        default="train",
+        type=str,
+        help="Split need to process.",
+    )
+    parser.add_argument(
+        "--grid_size",
+        default=None,
+        type=float,
+        help="Grid size for initial grid sampling",
+    )
+    parser.add_argument(
+        "--chunk_range",
+        default=[6, 6],
+        type=int,
+        nargs="+",
+        help="Range of each chunk, e.g. --chunk_range 6 6",
+    )
+    parser.add_argument(
+        "--chunk_stride",
+        default=[3, 3],
+        type=int,
+        nargs="+",
+        help="Stride of each chunk, e.g. --chunk_stride 3 3",
+    )
+    parser.add_argument(
+        "--chunk_minimum_size",
+        default=10000,
+        type=int,
+        help="Minimum number of points in each chunk",
+    )
+    parser.add_argument(
+        "--num_workers",
+        default=mp.cpu_count(),
+        type=int,
+        help="Num workers for preprocessing.",
+    )
+
+    config = parser.parse_args()
+    config.dataset_root = Path(config.dataset_root)
+    data_list = os.listdir(config.dataset_root / config.split)
+
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=config.num_workers)
+    _ = list(
+        pool.map(
+            chunking_scene,
+            data_list,
+            repeat(config.dataset_root),
+            repeat(config.split),
+            repeat(config.grid_size),
+            repeat(config.chunk_range),
+            repeat(config.chunk_stride),
+            repeat(config.chunk_minimum_size),
+        )
+    )
+    pool.shutdown()

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt

@@ -0,0 +1,17 @@
+1	trash
+3 	basket
+4	bathtub
+5	bed
+9	shelf
+13	cabinet
+18	chair
+20	keyboard
+22	tv
+30	lamp
+31	laptop
+35	microwave
+39	pillow
+42	printer
+47	sofa
+48	stove
+49	table

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt

@@ -0,0 +1,20 @@
+1       wall
+2       floor
+3       cabinet
+4       bed
+5       chair
+6       sofa
+7       table
+8       door
+9       window
+10      bookshelf
+11      picture
+12      counter
+14      desk
+16      curtain
+24      refridgerator
+28      shower curtain
+33      toilet
+34      sink
+36      bathtub
+39      otherfurniture

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py

@@ -0,0 +1,704 @@
+# ScanNet Benchmark constants
+VALID_CLASS_IDS_20 = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    14,
+    16,
+    24,
+    28,
+    33,
+    34,
+    36,
+    39,
+)
+
+CLASS_LABELS_20 = (
+    "wall",
+    "floor",
+    "cabinet",
+    "bed",
+    "chair",
+    "sofa",
+    "table",
+    "door",
+    "window",
+    "bookshelf",
+    "picture",
+    "counter",
+    "desk",
+    "curtain",
+    "refrigerator",
+    "shower curtain",
+    "toilet",
+    "sink",
+    "bathtub",
+    "otherfurniture",
+)
+
+SCANNET_COLOR_MAP_20 = {
+    0: (0.0, 0.0, 0.0),
+    1: (174.0, 199.0, 232.0),
+    2: (152.0, 223.0, 138.0),
+    3: (31.0, 119.0, 180.0),
+    4: (255.0, 187.0, 120.0),
+    5: (188.0, 189.0, 34.0),
+    6: (140.0, 86.0, 75.0),
+    7: (255.0, 152.0, 150.0),
+    8: (214.0, 39.0, 40.0),
+    9: (197.0, 176.0, 213.0),
+    10: (148.0, 103.0, 189.0),
+    11: (196.0, 156.0, 148.0),
+    12: (23.0, 190.0, 207.0),
+    14: (247.0, 182.0, 210.0),
+    15: (66.0, 188.0, 102.0),
+    16: (219.0, 219.0, 141.0),
+    17: (140.0, 57.0, 197.0),
+    18: (202.0, 185.0, 52.0),
+    19: (51.0, 176.0, 203.0),
+    20: (200.0, 54.0, 131.0),
+    21: (92.0, 193.0, 61.0),
+    22: (78.0, 71.0, 183.0),
+    23: (172.0, 114.0, 82.0),
+    24: (255.0, 127.0, 14.0),
+    25: (91.0, 163.0, 138.0),
+    26: (153.0, 98.0, 156.0),
+    27: (140.0, 153.0, 101.0),
+    28: (158.0, 218.0, 229.0),
+    29: (100.0, 125.0, 154.0),
+    30: (178.0, 127.0, 135.0),
+    32: (146.0, 111.0, 194.0),
+    33: (44.0, 160.0, 44.0),
+    34: (112.0, 128.0, 144.0),
+    35: (96.0, 207.0, 209.0),
+    36: (227.0, 119.0, 194.0),
+    37: (213.0, 92.0, 176.0),
+    38: (94.0, 106.0, 211.0),
+    39: (82.0, 84.0, 163.0),
+    40: (100.0, 85.0, 144.0),
+}
+
+# ScanNet200 Benchmark constants
+VALID_CLASS_IDS_200 = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    21,
+    22,
+    23,
+    24,
+    26,
+    27,
+    28,
+    29,
+    31,
+    32,
+    33,
+    34,
+    35,
+    36,
+    38,
+    39,
+    40,
+    41,
+    42,
+    44,
+    45,
+    46,
+    47,
+    48,
+    49,
+    50,
+    51,
+    52,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    62,
+    63,
+    64,
+    65,
+    66,
+    67,
+    68,
+    69,
+    70,
+    71,
+    72,
+    73,
+    74,
+    75,
+    76,
+    77,
+    78,
+    79,
+    80,
+    82,
+    84,
+    86,
+    87,
+    88,
+    89,
+    90,
+    93,
+    95,
+    96,
+    97,
+    98,
+    99,
+    100,
+    101,
+    102,
+    103,
+    104,
+    105,
+    106,
+    107,
+    110,
+    112,
+    115,
+    116,
+    118,
+    120,
+    121,
+    122,
+    125,
+    128,
+    130,
+    131,
+    132,
+    134,
+    136,
+    138,
+    139,
+    140,
+    141,
+    145,
+    148,
+    154,
+    155,
+    156,
+    157,
+    159,
+    161,
+    163,
+    165,
+    166,
+    168,
+    169,
+    170,
+    177,
+    180,
+    185,
+    188,
+    191,
+    193,
+    195,
+    202,
+    208,
+    213,
+    214,
+    221,
+    229,
+    230,
+    232,
+    233,
+    242,
+    250,
+    261,
+    264,
+    276,
+    283,
+    286,
+    300,
+    304,
+    312,
+    323,
+    325,
+    331,
+    342,
+    356,
+    370,
+    392,
+    395,
+    399,
+    408,
+    417,
+    488,
+    540,
+    562,
+    570,
+    572,
+    581,
+    609,
+    748,
+    776,
+    1156,
+    1163,
+    1164,
+    1165,
+    1166,
+    1167,
+    1168,
+    1169,
+    1170,
+    1171,
+    1172,
+    1173,
+    1174,
+    1175,
+    1176,
+    1178,
+    1179,
+    1180,
+    1181,
+    1182,
+    1183,
+    1184,
+    1185,
+    1186,
+    1187,
+    1188,
+    1189,
+    1190,
+    1191,
+)
+
+CLASS_LABELS_200 = (
+    "wall",
+    "chair",
+    "floor",
+    "table",
+    "door",
+    "couch",
+    "cabinet",
+    "shelf",
+    "desk",
+    "office chair",
+    "bed",
+    "pillow",
+    "sink",
+    "picture",
+    "window",
+    "toilet",
+    "bookshelf",
+    "monitor",
+    "curtain",
+    "book",
+    "armchair",
+    "coffee table",
+    "box",
+    "refrigerator",
+    "lamp",
+    "kitchen cabinet",
+    "towel",
+    "clothes",
+    "tv",
+    "nightstand",
+    "counter",
+    "dresser",
+    "stool",
+    "cushion",
+    "plant",
+    "ceiling",
+    "bathtub",
+    "end table",
+    "dining table",
+    "keyboard",
+    "bag",
+    "backpack",
+    "toilet paper",
+    "printer",
+    "tv stand",
+    "whiteboard",
+    "blanket",
+    "shower curtain",
+    "trash can",
+    "closet",
+    "stairs",
+    "microwave",
+    "stove",
+    "shoe",
+    "computer tower",
+    "bottle",
+    "bin",
+    "ottoman",
+    "bench",
+    "board",
+    "washing machine",
+    "mirror",
+    "copier",
+    "basket",
+    "sofa chair",
+    "file cabinet",
+    "fan",
+    "laptop",
+    "shower",
+    "paper",
+    "person",
+    "paper towel dispenser",
+    "oven",
+    "blinds",
+    "rack",
+    "plate",
+    "blackboard",
+    "piano",
+    "suitcase",
+    "rail",
+    "radiator",
+    "recycling bin",
+    "container",
+    "wardrobe",
+    "soap dispenser",
+    "telephone",
+    "bucket",
+    "clock",
+    "stand",
+    "light",
+    "laundry basket",
+    "pipe",
+    "clothes dryer",
+    "guitar",
+    "toilet paper holder",
+    "seat",
+    "speaker",
+    "column",
+    "bicycle",
+    "ladder",
+    "bathroom stall",
+    "shower wall",
+    "cup",
+    "jacket",
+    "storage bin",
+    "coffee maker",
+    "dishwasher",
+    "paper towel roll",
+    "machine",
+    "mat",
+    "windowsill",
+    "bar",
+    "toaster",
+    "bulletin board",
+    "ironing board",
+    "fireplace",
+    "soap dish",
+    "kitchen counter",
+    "doorframe",
+    "toilet paper dispenser",
+    "mini fridge",
+    "fire extinguisher",
+    "ball",
+    "hat",
+    "shower curtain rod",
+    "water cooler",
+    "paper cutter",
+    "tray",
+    "shower door",
+    "pillar",
+    "ledge",
+    "toaster oven",
+    "mouse",
+    "toilet seat cover dispenser",
+    "furniture",
+    "cart",
+    "storage container",
+    "scale",
+    "tissue box",
+    "light switch",
+    "crate",
+    "power outlet",
+    "decoration",
+    "sign",
+    "projector",
+    "closet door",
+    "vacuum cleaner",
+    "candle",
+    "plunger",
+    "stuffed animal",
+    "headphones",
+    "dish rack",
+    "broom",
+    "guitar case",
+    "range hood",
+    "dustpan",
+    "hair dryer",
+    "water bottle",
+    "handicap bar",
+    "purse",
+    "vent",
+    "shower floor",
+    "water pitcher",
+    "mailbox",
+    "bowl",
+    "paper bag",
+    "alarm clock",
+    "music stand",
+    "projector screen",
+    "divider",
+    "laundry detergent",
+    "bathroom counter",
+    "object",
+    "bathroom vanity",
+    "closet wall",
+    "laundry hamper",
+    "bathroom stall door",
+    "ceiling light",
+    "trash bin",
+    "dumbbell",
+    "stair rail",
+    "tube",
+    "bathroom cabinet",
+    "cd case",
+    "closet rod",
+    "coffee kettle",
+    "structure",
+    "shower head",
+    "keyboard piano",
+    "case of water bottles",
+    "coat rack",
+    "storage organizer",
+    "folded chair",
+    "fire alarm",
+    "power strip",
+    "calendar",
+    "poster",
+    "potted plant",
+    "luggage",
+    "mattress",
+)
+
+SCANNET_COLOR_MAP_200 = {
+    0: (0.0, 0.0, 0.0),
+    1: (174.0, 199.0, 232.0),
+    2: (188.0, 189.0, 34.0),
+    3: (152.0, 223.0, 138.0),
+    4: (255.0, 152.0, 150.0),
+    5: (214.0, 39.0, 40.0),
+    6: (91.0, 135.0, 229.0),
+    7: (31.0, 119.0, 180.0),
+    8: (229.0, 91.0, 104.0),
+    9: (247.0, 182.0, 210.0),
+    10: (91.0, 229.0, 110.0),
+    11: (255.0, 187.0, 120.0),
+    13: (141.0, 91.0, 229.0),
+    14: (112.0, 128.0, 144.0),
+    15: (196.0, 156.0, 148.0),
+    16: (197.0, 176.0, 213.0),
+    17: (44.0, 160.0, 44.0),
+    18: (148.0, 103.0, 189.0),
+    19: (229.0, 91.0, 223.0),
+    21: (219.0, 219.0, 141.0),
+    22: (192.0, 229.0, 91.0),
+    23: (88.0, 218.0, 137.0),
+    24: (58.0, 98.0, 137.0),
+    26: (177.0, 82.0, 239.0),
+    27: (255.0, 127.0, 14.0),
+    28: (237.0, 204.0, 37.0),
+    29: (41.0, 206.0, 32.0),
+    31: (62.0, 143.0, 148.0),
+    32: (34.0, 14.0, 130.0),
+    33: (143.0, 45.0, 115.0),
+    34: (137.0, 63.0, 14.0),
+    35: (23.0, 190.0, 207.0),
+    36: (16.0, 212.0, 139.0),
+    38: (90.0, 119.0, 201.0),
+    39: (125.0, 30.0, 141.0),
+    40: (150.0, 53.0, 56.0),
+    41: (186.0, 197.0, 62.0),
+    42: (227.0, 119.0, 194.0),
+    44: (38.0, 100.0, 128.0),
+    45: (120.0, 31.0, 243.0),
+    46: (154.0, 59.0, 103.0),
+    47: (169.0, 137.0, 78.0),
+    48: (143.0, 245.0, 111.0),
+    49: (37.0, 230.0, 205.0),
+    50: (14.0, 16.0, 155.0),
+    51: (196.0, 51.0, 182.0),
+    52: (237.0, 80.0, 38.0),
+    54: (138.0, 175.0, 62.0),
+    55: (158.0, 218.0, 229.0),
+    56: (38.0, 96.0, 167.0),
+    57: (190.0, 77.0, 246.0),
+    58: (208.0, 49.0, 84.0),
+    59: (208.0, 193.0, 72.0),
+    62: (55.0, 220.0, 57.0),
+    63: (10.0, 125.0, 140.0),
+    64: (76.0, 38.0, 202.0),
+    65: (191.0, 28.0, 135.0),
+    66: (211.0, 120.0, 42.0),
+    67: (118.0, 174.0, 76.0),
+    68: (17.0, 242.0, 171.0),
+    69: (20.0, 65.0, 247.0),
+    70: (208.0, 61.0, 222.0),
+    71: (162.0, 62.0, 60.0),
+    72: (210.0, 235.0, 62.0),
+    73: (45.0, 152.0, 72.0),
+    74: (35.0, 107.0, 149.0),
+    75: (160.0, 89.0, 237.0),
+    76: (227.0, 56.0, 125.0),
+    77: (169.0, 143.0, 81.0),
+    78: (42.0, 143.0, 20.0),
+    79: (25.0, 160.0, 151.0),
+    80: (82.0, 75.0, 227.0),
+    82: (253.0, 59.0, 222.0),
+    84: (240.0, 130.0, 89.0),
+    86: (123.0, 172.0, 47.0),
+    87: (71.0, 194.0, 133.0),
+    88: (24.0, 94.0, 205.0),
+    89: (134.0, 16.0, 179.0),
+    90: (159.0, 32.0, 52.0),
+    93: (213.0, 208.0, 88.0),
+    95: (64.0, 158.0, 70.0),
+    96: (18.0, 163.0, 194.0),
+    97: (65.0, 29.0, 153.0),
+    98: (177.0, 10.0, 109.0),
+    99: (152.0, 83.0, 7.0),
+    100: (83.0, 175.0, 30.0),
+    101: (18.0, 199.0, 153.0),
+    102: (61.0, 81.0, 208.0),
+    103: (213.0, 85.0, 216.0),
+    104: (170.0, 53.0, 42.0),
+    105: (161.0, 192.0, 38.0),
+    106: (23.0, 241.0, 91.0),
+    107: (12.0, 103.0, 170.0),
+    110: (151.0, 41.0, 245.0),
+    112: (133.0, 51.0, 80.0),
+    115: (184.0, 162.0, 91.0),
+    116: (50.0, 138.0, 38.0),
+    118: (31.0, 237.0, 236.0),
+    120: (39.0, 19.0, 208.0),
+    121: (223.0, 27.0, 180.0),
+    122: (254.0, 141.0, 85.0),
+    125: (97.0, 144.0, 39.0),
+    128: (106.0, 231.0, 176.0),
+    130: (12.0, 61.0, 162.0),
+    131: (124.0, 66.0, 140.0),
+    132: (137.0, 66.0, 73.0),
+    134: (250.0, 253.0, 26.0),
+    136: (55.0, 191.0, 73.0),
+    138: (60.0, 126.0, 146.0),
+    139: (153.0, 108.0, 234.0),
+    140: (184.0, 58.0, 125.0),
+    141: (135.0, 84.0, 14.0),
+    145: (139.0, 248.0, 91.0),
+    148: (53.0, 200.0, 172.0),
+    154: (63.0, 69.0, 134.0),
+    155: (190.0, 75.0, 186.0),
+    156: (127.0, 63.0, 52.0),
+    157: (141.0, 182.0, 25.0),
+    159: (56.0, 144.0, 89.0),
+    161: (64.0, 160.0, 250.0),
+    163: (182.0, 86.0, 245.0),
+    165: (139.0, 18.0, 53.0),
+    166: (134.0, 120.0, 54.0),
+    168: (49.0, 165.0, 42.0),
+    169: (51.0, 128.0, 133.0),
+    170: (44.0, 21.0, 163.0),
+    177: (232.0, 93.0, 193.0),
+    180: (176.0, 102.0, 54.0),
+    185: (116.0, 217.0, 17.0),
+    188: (54.0, 209.0, 150.0),
+    191: (60.0, 99.0, 204.0),
+    193: (129.0, 43.0, 144.0),
+    195: (252.0, 100.0, 106.0),
+    202: (187.0, 196.0, 73.0),
+    208: (13.0, 158.0, 40.0),
+    213: (52.0, 122.0, 152.0),
+    214: (128.0, 76.0, 202.0),
+    221: (187.0, 50.0, 115.0),
+    229: (180.0, 141.0, 71.0),
+    230: (77.0, 208.0, 35.0),
+    232: (72.0, 183.0, 168.0),
+    233: (97.0, 99.0, 203.0),
+    242: (172.0, 22.0, 158.0),
+    250: (155.0, 64.0, 40.0),
+    261: (118.0, 159.0, 30.0),
+    264: (69.0, 252.0, 148.0),
+    276: (45.0, 103.0, 173.0),
+    283: (111.0, 38.0, 149.0),
+    286: (184.0, 9.0, 49.0),
+    300: (188.0, 174.0, 67.0),
+    304: (53.0, 206.0, 53.0),
+    312: (97.0, 235.0, 252.0),
+    323: (66.0, 32.0, 182.0),
+    325: (236.0, 114.0, 195.0),
+    331: (241.0, 154.0, 83.0),
+    342: (133.0, 240.0, 52.0),
+    356: (16.0, 205.0, 144.0),
+    370: (75.0, 101.0, 198.0),
+    392: (237.0, 95.0, 251.0),
+    395: (191.0, 52.0, 49.0),
+    399: (227.0, 254.0, 54.0),
+    408: (49.0, 206.0, 87.0),
+    417: (48.0, 113.0, 150.0),
+    488: (125.0, 73.0, 182.0),
+    540: (229.0, 32.0, 114.0),
+    562: (158.0, 119.0, 28.0),
+    570: (60.0, 205.0, 27.0),
+    572: (18.0, 215.0, 201.0),
+    581: (79.0, 76.0, 153.0),
+    609: (134.0, 13.0, 116.0),
+    748: (192.0, 97.0, 63.0),
+    776: (108.0, 163.0, 18.0),
+    1156: (95.0, 220.0, 156.0),
+    1163: (98.0, 141.0, 208.0),
+    1164: (144.0, 19.0, 193.0),
+    1165: (166.0, 36.0, 57.0),
+    1166: (212.0, 202.0, 34.0),
+    1167: (23.0, 206.0, 34.0),
+    1168: (91.0, 211.0, 236.0),
+    1169: (79.0, 55.0, 137.0),
+    1170: (182.0, 19.0, 117.0),
+    1171: (134.0, 76.0, 14.0),
+    1172: (87.0, 185.0, 28.0),
+    1173: (82.0, 224.0, 187.0),
+    1174: (92.0, 110.0, 214.0),
+    1175: (168.0, 80.0, 171.0),
+    1176: (197.0, 63.0, 51.0),
+    1178: (175.0, 199.0, 77.0),
+    1179: (62.0, 180.0, 98.0),
+    1180: (8.0, 91.0, 150.0),
+    1181: (77.0, 15.0, 130.0),
+    1182: (154.0, 65.0, 96.0),
+    1183: (197.0, 152.0, 11.0),
+    1184: (59.0, 155.0, 45.0),
+    1185: (12.0, 147.0, 145.0),
+    1186: (54.0, 35.0, 219.0),
+    1187: (210.0, 73.0, 181.0),
+    1188: (221.0, 124.0, 77.0),
+    1189: (149.0, 214.0, 66.0),
+    1190: (72.0, 185.0, 134.0),
+    1191: (42.0, 94.0, 198.0),
+}
+
+# For instance segmentation the non-object categories
+VALID_PANOPTIC_IDS = (1, 3)
+
+CLASS_LABELS_PANOPTIC = ("wall", "floor")

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py

@@ -0,0 +1,625 @@
+# This file contains the HEAD - COMMON - TAIL split category ids for ScanNet 200
+
+HEAD_CATS_SCANNET_200 = [
+    "tv stand",
+    "curtain",
+    "blinds",
+    "shower curtain",
+    "bookshelf",
+    "tv",
+    "kitchen cabinet",
+    "pillow",
+    "lamp",
+    "dresser",
+    "monitor",
+    "object",
+    "ceiling",
+    "board",
+    "stove",
+    "closet wall",
+    "couch",
+    "office chair",
+    "kitchen counter",
+    "shower",
+    "closet",
+    "doorframe",
+    "sofa chair",
+    "mailbox",
+    "nightstand",
+    "washing machine",
+    "picture",
+    "book",
+    "sink",
+    "recycling bin",
+    "table",
+    "backpack",
+    "shower wall",
+    "toilet",
+    "copier",
+    "counter",
+    "stool",
+    "refrigerator",
+    "window",
+    "file cabinet",
+    "chair",
+    "wall",
+    "plant",
+    "coffee table",
+    "stairs",
+    "armchair",
+    "cabinet",
+    "bathroom vanity",
+    "bathroom stall",
+    "mirror",
+    "blackboard",
+    "trash can",
+    "stair rail",
+    "box",
+    "towel",
+    "door",
+    "clothes",
+    "whiteboard",
+    "bed",
+    "floor",
+    "bathtub",
+    "desk",
+    "wardrobe",
+    "clothes dryer",
+    "radiator",
+    "shelf",
+]
+COMMON_CATS_SCANNET_200 = [
+    "cushion",
+    "end table",
+    "dining table",
+    "keyboard",
+    "bag",
+    "toilet paper",
+    "printer",
+    "blanket",
+    "microwave",
+    "shoe",
+    "computer tower",
+    "bottle",
+    "bin",
+    "ottoman",
+    "bench",
+    "basket",
+    "fan",
+    "laptop",
+    "person",
+    "paper towel dispenser",
+    "oven",
+    "rack",
+    "piano",
+    "suitcase",
+    "rail",
+    "container",
+    "telephone",
+    "stand",
+    "light",
+    "laundry basket",
+    "pipe",
+    "seat",
+    "column",
+    "bicycle",
+    "ladder",
+    "jacket",
+    "storage bin",
+    "coffee maker",
+    "dishwasher",
+    "machine",
+    "mat",
+    "windowsill",
+    "bulletin board",
+    "fireplace",
+    "mini fridge",
+    "water cooler",
+    "shower door",
+    "pillar",
+    "ledge",
+    "furniture",
+    "cart",
+    "decoration",
+    "closet door",
+    "vacuum cleaner",
+    "dish rack",
+    "range hood",
+    "projector screen",
+    "divider",
+    "bathroom counter",
+    "laundry hamper",
+    "bathroom stall door",
+    "ceiling light",
+    "trash bin",
+    "bathroom cabinet",
+    "structure",
+    "storage organizer",
+    "potted plant",
+    "mattress",
+]
+TAIL_CATS_SCANNET_200 = [
+    "paper",
+    "plate",
+    "soap dispenser",
+    "bucket",
+    "clock",
+    "guitar",
+    "toilet paper holder",
+    "speaker",
+    "cup",
+    "paper towel roll",
+    "bar",
+    "toaster",
+    "ironing board",
+    "soap dish",
+    "toilet paper dispenser",
+    "fire extinguisher",
+    "ball",
+    "hat",
+    "shower curtain rod",
+    "paper cutter",
+    "tray",
+    "toaster oven",
+    "mouse",
+    "toilet seat cover dispenser",
+    "storage container",
+    "scale",
+    "tissue box",
+    "light switch",
+    "crate",
+    "power outlet",
+    "sign",
+    "projector",
+    "candle",
+    "plunger",
+    "stuffed animal",
+    "headphones",
+    "broom",
+    "guitar case",
+    "dustpan",
+    "hair dryer",
+    "water bottle",
+    "handicap bar",
+    "purse",
+    "vent",
+    "shower floor",
+    "water pitcher",
+    "bowl",
+    "paper bag",
+    "alarm clock",
+    "music stand",
+    "laundry detergent",
+    "dumbbell",
+    "tube",
+    "cd case",
+    "closet rod",
+    "coffee kettle",
+    "shower head",
+    "keyboard piano",
+    "case of water bottles",
+    "coat rack",
+    "folded chair",
+    "fire alarm",
+    "power strip",
+    "calendar",
+    "poster",
+    "luggage",
+]
+
+
+# Given the different size of the official train and val sets, not all ScanNet200 categories are present in the validation set.
+# Here we list of categories with labels and IDs present in both train and validation set, and the remaining categories those are present in train, but not in val
+# We dont evaluate on unseen validation categories in this benchmark
+
+VALID_CLASS_IDS_200_VALIDATION = (
+    "wall",
+    "chair",
+    "floor",
+    "table",
+    "door",
+    "couch",
+    "cabinet",
+    "shelf",
+    "desk",
+    "office chair",
+    "bed",
+    "pillow",
+    "sink",
+    "picture",
+    "window",
+    "toilet",
+    "bookshelf",
+    "monitor",
+    "curtain",
+    "book",
+    "armchair",
+    "coffee table",
+    "box",
+    "refrigerator",
+    "lamp",
+    "kitchen cabinet",
+    "towel",
+    "clothes",
+    "tv",
+    "nightstand",
+    "counter",
+    "dresser",
+    "stool",
+    "cushion",
+    "plant",
+    "ceiling",
+    "bathtub",
+    "end table",
+    "dining table",
+    "keyboard",
+    "bag",
+    "backpack",
+    "toilet paper",
+    "printer",
+    "tv stand",
+    "whiteboard",
+    "blanket",
+    "shower curtain",
+    "trash can",
+    "closet",
+    "stairs",
+    "microwave",
+    "stove",
+    "shoe",
+    "computer tower",
+    "bottle",
+    "bin",
+    "ottoman",
+    "bench",
+    "board",
+    "washing machine",
+    "mirror",
+    "copier",
+    "basket",
+    "sofa chair",
+    "file cabinet",
+    "fan",
+    "laptop",
+    "shower",
+    "paper",
+    "person",
+    "paper towel dispenser",
+    "oven",
+    "blinds",
+    "rack",
+    "plate",
+    "blackboard",
+    "piano",
+    "suitcase",
+    "rail",
+    "radiator",
+    "recycling bin",
+    "container",
+    "wardrobe",
+    "soap dispenser",
+    "telephone",
+    "bucket",
+    "clock",
+    "stand",
+    "light",
+    "laundry basket",
+    "pipe",
+    "clothes dryer",
+    "guitar",
+    "toilet paper holder",
+    "seat",
+    "speaker",
+    "column",
+    "ladder",
+    "bathroom stall",
+    "shower wall",
+    "cup",
+    "jacket",
+    "storage bin",
+    "coffee maker",
+    "dishwasher",
+    "paper towel roll",
+    "machine",
+    "mat",
+    "windowsill",
+    "bar",
+    "toaster",
+    "bulletin board",
+    "ironing board",
+    "fireplace",
+    "soap dish",
+    "kitchen counter",
+    "doorframe",
+    "toilet paper dispenser",
+    "mini fridge",
+    "fire extinguisher",
+    "ball",
+    "hat",
+    "shower curtain rod",
+    "water cooler",
+    "paper cutter",
+    "tray",
+    "shower door",
+    "pillar",
+    "ledge",
+    "toaster oven",
+    "mouse",
+    "toilet seat cover dispenser",
+    "furniture",
+    "cart",
+    "scale",
+    "tissue box",
+    "light switch",
+    "crate",
+    "power outlet",
+    "decoration",
+    "sign",
+    "projector",
+    "closet door",
+    "vacuum cleaner",
+    "plunger",
+    "stuffed animal",
+    "headphones",
+    "dish rack",
+    "broom",
+    "range hood",
+    "dustpan",
+    "hair dryer",
+    "water bottle",
+    "handicap bar",
+    "vent",
+    "shower floor",
+    "water pitcher",
+    "mailbox",
+    "bowl",
+    "paper bag",
+    "projector screen",
+    "divider",
+    "laundry detergent",
+    "bathroom counter",
+    "object",
+    "bathroom vanity",
+    "closet wall",
+    "laundry hamper",
+    "bathroom stall door",
+    "ceiling light",
+    "trash bin",
+    "dumbbell",
+    "stair rail",
+    "tube",
+    "bathroom cabinet",
+    "closet rod",
+    "coffee kettle",
+    "shower head",
+    "keyboard piano",
+    "case of water bottles",
+    "coat rack",
+    "folded chair",
+    "fire alarm",
+    "power strip",
+    "calendar",
+    "poster",
+    "potted plant",
+    "mattress",
+)
+
+CLASS_LABELS_200_VALIDATION = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    21,
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+    40,
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+    54,
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+    56,
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+    58,
+    59,
+    62,
+    63,
+    64,
+    65,
+    66,
+    67,
+    68,
+    69,
+    70,
+    71,
+    72,
+    73,
+    74,
+    75,
+    76,
+    77,
+    78,
+    79,
+    80,
+    82,
+    84,
+    86,
+    87,
+    88,
+    89,
+    90,
+    93,
+    95,
+    96,
+    97,
+    98,
+    99,
+    100,
+    101,
+    102,
+    103,
+    104,
+    105,
+    106,
+    107,
+    110,
+    112,
+    115,
+    116,
+    118,
+    120,
+    122,
+    125,
+    128,
+    130,
+    131,
+    132,
+    134,
+    136,
+    138,
+    139,
+    140,
+    141,
+    145,
+    148,
+    154,
+    155,
+    156,
+    157,
+    159,
+    161,
+    163,
+    165,
+    166,
+    168,
+    169,
+    170,
+    177,
+    180,
+    185,
+    188,
+    191,
+    193,
+    195,
+    202,
+    208,
+    213,
+    214,
+    229,
+    230,
+    232,
+    233,
+    242,
+    250,
+    261,
+    264,
+    276,
+    283,
+    300,
+    304,
+    312,
+    323,
+    325,
+    342,
+    356,
+    370,
+    392,
+    395,
+    408,
+    417,
+    488,
+    540,
+    562,
+    570,
+    609,
+    748,
+    776,
+    1156,
+    1163,
+    1164,
+    1165,
+    1166,
+    1167,
+    1168,
+    1169,
+    1170,
+    1171,
+    1172,
+    1173,
+    1175,
+    1176,
+    1179,
+    1180,
+    1181,
+    1182,
+    1184,
+    1185,
+    1186,
+    1187,
+    1188,
+    1189,
+    1191,
+)
+
+VALID_CLASS_IDS_200_TRAIN_ONLY = (
+    "bicycle",
+    "storage container",
+    "candle",
+    "guitar case",
+    "purse",
+    "alarm clock",
+    "music stand",
+    "cd case",
+    "structure",
+    "storage organizer",
+    "luggage",
+)
+
+CLASS_LABELS_200_TRAIN_ONLY = (
+    121,
+    221,
+    286,
+    331,
+    399,
+    572,
+    581,
+    1174,
+    1178,
+    1183,
+    1190,
+)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz

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+size 676

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scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels-old.combined.tsv

@@ -0,0 +1,608 @@
+id	raw_category	category	count	nyu40id	eigen13id	nyuClass	nyu40class	eigen13class	ModelNet40	ModelNet10	ShapeNetCore55	synsetoffset	wnsynsetid	wnsynsetkey	mpcat40	mpcat40index
+1	wall	wall	8277	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+2	chair	chair	4646	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+22	books	book	1678	23	2	book	books	Books					n02870526	book.n.11	objects	39
+3	floor	floor	1553	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+5	door	door	1483	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+1163	object	object	1313	40	7		otherprop	Objects							objects	39
+16	window	window	1209	9	13	window	window	Window					n04587648	window.n.01	window	9
+4	table	table	1170	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+56	trash can	trash can	1090	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+13	pillow	pillow	937	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+15	picture	picture	862	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+41	ceiling	ceiling	806	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+26	box	box	775	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+161	doorframe	doorframe	768	8	12	door	door	Wall	door					doorframe.n.01	door	4
+19	monitor	monitor	765	40	7	monitor	otherprop	Objects	monitor	monitor	tv or monitor	3211117	n03782190	monitor.n.04	objects	39
+7	cabinet	cabinet	731	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+9	desk	desk	680	14	10	desk	desk	Table	desk	desk	table	4379243	n03179701	desk.n.01	table	5
+8	shelf	shelf	641	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+10	office chair	office chair	595	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04373704	swivel_chair.n.01	chair	3
+31	towel	towel	570	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+6	couch	couch	502	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+14	sink	sink	488	34	7	sink	sink	Objects	sink				n04223580	sink.n.01	sink	15
+48	backpack	backpack	479	40	7	backpack	otherprop	Objects					n02769748	backpack.n.01	objects	39
+28	lamp	lamp	419	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+11	bed	bed	370	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+18	bookshelf	bookshelf	360	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+71	mirror	mirror	349	19	7	mirror	mirror	Objects					n03773035	mirror.n.01	mirror	21
+21	curtain	curtain	347	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+40	plant	plant	331	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+52	whiteboard	whiteboard	327	30	7	whiteboard	whiteboard	Objects					n03211616	display_panel.n.01	board_panel	35
+96	radiator	radiator	322	39	6	radiator	otherfurniture	Furniture					n04041069	radiator.n.02	misc	40
+22	book	book	318	23	2	book	books	Books					n02870526	book.n.11	objects	39
+29	kitchen cabinet	kitchen cabinet	310	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+49	toilet paper	toilet paper	291	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+29	kitchen cabinets	kitchen cabinet	289	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+23	armchair	armchair	281	5	4	chair	chair	Chair	chair	chair	chair	3001627	n02738535	armchair.n.01	chair	3
+63	shoes	shoe	272	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+24	coffee table	coffee table	258	7	10	coffee table	table	Table	table	table	table	4379243	n03063968	coffee_table.n.01	table	5
+17	toilet	toilet	256	33	7	toilet	toilet	Objects	toilet	toilet			n04446276	toilet.n.01	toilet	18
+47	bag	bag	252	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+32	clothes	clothes	248	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+46	keyboard	keyboard	246	40	7	keyboard	otherprop	Objects	keyboard		computer keyboard	3085013	n03085013	computer_keyboard.n.01	objects	39
+65	bottle	bottle	226	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+97	recycling bin	recycling bin	225	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+34	nightstand	nightstand	224	32	6	night stand	night stand	Furniture	night_stand	night_stand			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+38	stool	stool	221	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+33	tv	tv	219	25	11	television	television	TV			tv or monitor	3211117	n03211117	display.n.06	tv_monitor	22
+75	file cabinet	file cabinet	217	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+36	dresser	dresser	213	17	6	dresser	dresser	Furniture	dresser	dresser			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+64	computer tower	computer tower	203	40	7	computer	otherprop	Objects					n03082979	computer.n.01	objects	39
+32	clothing	clothes	165	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+101	telephone	telephone	164	40	7	telephone	otherprop	Objects			telephone	4401088	n04401088	telephone.n.01	objects	39
+130	cup	cup	157	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+27	refrigerator	refrigerator	154	24	6	refridgerator	refridgerator	Furniture					n04070727	refrigerator.n.01	appliances	37
+44	end table	end table	147	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+131	jacket	jacket	146	40	7	jacket	otherprop	Objects					n03589791	jacket.n.01	clothes	38
+55	shower curtain	shower curtain	144	28	7	shower curtain	shower curtain	Objects	curtain				n04209239	shower_curtain.n.01	curtain	12
+42	bathtub	bathtub	144	36	7	bathtub	bathtub	Objects	bathtub	bathtub	tub	2808440	n02808440	bathtub.n.01	bathtub	25
+59	microwave	microwave	141	40	7	microwave	otherprop	Objects			microwave	3761084	n03761084	microwave.n.02	appliances	37
+159	kitchen counter	kitchen counter	140	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+74	sofa chair	sofa chair	129	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+82	paper towel dispenser	paper towel dispenser	129	40	7	paper towel dispenser	otherprop	Objects							objects	39
+1164	bathroom vanity	bathroom vanity	126	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	table	5
+93	suitcase	suitcase	118	40	7	luggage	otherprop	Objects					n02773838	bag.n.06	objects	39
+77	laptop	laptop	111	40	7	laptop	otherprop	Objects	laptop		laptop	3642806	n03642806	laptop.n.01	objects	39
+67	ottoman	ottoman	111	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+128	shower walls	shower wall	109	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+50	printer	printer	106	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+35	counter	counter	104	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+69	board	board	100	38	7	board	otherstructure	Objects							board_panel	35
+100	soap dispenser	soap dispenser	99	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+62	stove	stove	95	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+105	light	light	93	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+1165	closet wall	closet wall	90	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+165	mini fridge	mini fridge	87	24	6	refridgerator	refridgerator	Furniture					n03273913	electric_refrigerator.n.01	appliances	37
+7	cabinets	cabinet	79	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+5	doors	door	76	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+76	fan	fan	75	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+230	tissue box	tissue box	73	40	7	tissue box	otherprop	Objects					n02883344	box.n.01	objects	39
+54	blanket	blanket	72	40	7	blanket	otherprop	Objects					n02849154	blanket.n.01	objects	39
+125	bathroom stall	bathroom stall	71	38	7		otherstructure	Objects					n02873839	booth.n.02	misc	40
+72	copier	copier	70	40	7		otherprop	Objects					n03257586	duplicator.n.01	appliances	37
+68	bench	bench	66	39	6	bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+145	bar	bar	66	38	7	bar	otherstructure	Objects					n02788689	bar.n.03	misc	40
+157	soap dish	soap dish	65	40	7	soap dish	otherprop	Objects					n04254009	soap_dish.n.01	objects	39
+1166	laundry hamper	laundry hamper	65	40	7	laundry basket	otherprop	Objects							objects	39
+132	storage bin	storage bin	63	40	7	storage bin	otherprop	Objects							objects	39
+1167	bathroom stall door	bathroom stall door	62	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+232	light switch	light switch	61	38	7	light switch	otherstructure	Objects					n04372370	switch.n.01	misc	40
+134	coffee maker	coffee maker	61	40	7		otherprop	Objects					n03063338	coffee_maker.n.01	appliances	37
+51	tv stand	tv stand	61	39	6	tv stand	otherfurniture	Furniture	tv_stand				n03290653	entertainment_center.n.01	furniture	36
+250	decoration	decoration	60	40	7		otherprop	Objects					n03169390	decoration.n.01	misc	40
+1168	ceiling light	ceiling light	59	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+342	range hood	range hood	59	38	7	range hood	otherstructure	Objects	range_hood				n04053677	range_hood.n.01	misc	40
+89	blackboard	blackboard	58	38	7	blackboard	otherstructure	Objects					n02846511	blackboard.n.01	board_panel	35
+103	clock	clock	58	40	7	clock	otherprop	Objects			clock	3046257	n03046257	clock.n.01	objects	39
+99	wardrobe closet	wardrobe	54	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+95	rail	rail	53	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+154	bulletin board	bulletin board	53	38	7	board	otherstructure	Objects					n03211616	display_panel.n.01	board_panel	35
+140	mat	mat	52	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1169	trash bin	trash bin	52	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+193	ledge	ledge	51	38	7		otherstructure	Objects					n09337253	ledge.n.01	misc	40
+116	seat	seat	49	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+202	mouse	mouse	49	40	7	mouse	otherprop	Objects					n03793489	mouse.n.04	objects	39
+73	basket	basket	48	40	7	basket	otherprop	Objects			basket	2801938	n02801938	basket.n.01	objects	39
+78	shower	shower	48	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+1170	dumbbell	dumbbell	48	40	7		otherprop	Objects					n03255030	dumbbell.n.01	objects	39
+79	paper	paper	46	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+80	person	person	46	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+141	windowsill	windowsill	45	38	7		otherstructure	Objects					n04590263	windowsill.n.01	window	9
+57	closet	closet	45	39	6	wardrobe	otherfurniture	Furniture	wardrobe						misc	40
+102	bucket	bucket	45	40	7	bucket	otherprop	Objects					n02909870	bucket.n.01	misc	40
+261	sign	sign	44	40	7	sign	otherprop	Objects					n04217882	signboard.n.01	objects	39
+118	speaker	speaker	43	40	7	speaker	otherprop	Objects			speaker	3691459	n03691459	loudspeaker.n.01	objects	39
+136	dishwasher	dishwasher	43	38	7	dishwasher	otherstructure	Objects			dishwasher	3207941	n03207941	dishwasher.n.01	appliances	37
+98	container	container	43	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1171	stair rail	stair rail	42	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+170	shower curtain rod	shower curtain rod	42	40	7		otherprop	Objects							curtain	12
+1172	tube	tube	41	40	7		otherprop	Objects							misc	40
+1173	bathroom cabinet	bathroom cabinet	39	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+79	papers	paper	39	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+221	storage container	storage container	39	40	7	container	otherprop	Objects							objects	39
+570	paper bag	paper bag	39	37	7	bag	bag	Objects					n04122825	sack.n.01	objects	39
+138	paper towel roll	paper towel roll	39	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+168	ball	ball	39	40	7	ball	otherprop	Objects							objects	39
+276	closet doors	closet door	38	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+106	laundry basket	laundry basket	37	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+214	cart	cart	37	40	7	cart	otherprop	Objects					n03484083	handcart.n.01	shelving	31
+276	closet door	closet door	35	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+323	dish rack	dish rack	35	40	7	dish rack	otherprop	Objects					n03207630	dish_rack.n.01	objects	39
+58	stairs	stairs	35	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+86	blinds	blinds	35	13	13	blinds	blinds	Window					n02851099	blind.n.03	blinds	32
+2	stack of chairs	chair	35	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+399	purse	purse	34	40	7	purse	otherprop	Objects					n02774152	bag.n.04	objects	39
+121	bicycle	bicycle	33	40	7	bicycle	otherprop	Objects			bicycle	2834778	n02834778	bicycle.n.01	objects	39
+185	tray	tray	32	40	7	tray	otherprop	Objects					n04476259	tray.n.01	objects	39
+300	plunger	plunger	30	40	7		otherprop	Objects					n03970156	plunger.n.03	objects	39
+180	paper cutter	paper cutter	30	40	7	paper cutter	otherprop	Objects					n03886940	paper_cutter.n.01	objects	39
+163	toilet paper dispenser	toilet paper dispenser	29	40	7		otherprop	Objects							objects	39
+26	boxes	box	29	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+66	bin	bin	28	40	7	bin	otherprop	Objects					n02839910	bin.n.01	objects	39
+208	toilet seat cover dispenser	toilet seat cover dispenser	28	40	7		otherprop	Objects							objects	39
+112	guitar	guitar	28	40	7	guitar	otherprop	Objects	guitar		guitar	3467517	n03467517	guitar.n.01	objects	39
+540	mailboxes	mailbox	28	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+395	handicap bar	handicap bar	27	38	7	bar	otherstructure	Objects							misc	40
+166	fire extinguisher	fire extinguisher	27	40	7	fire extinguisher	otherprop	Objects					n03345837	fire_extinguisher.n.01	misc	40
+122	ladder	ladder	27	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	stairs	16
+120	column	column	26	38	7	column	otherstructure	Objects					n03074380	column.n.06	column	24
+107	pipe	pipe	25	40	7	pipe	otherprop	Objects					n03944672	pipe.n.02	misc	40
+283	vacuum cleaner	vacuum cleaner	25	40	7		otherprop	Objects					n04517823	vacuum.n.04	objects	39
+88	plate	plate	24	40	7	plate	otherprop	Objects					n03959485	plate.n.04	objects	39
+90	piano	piano	24	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+177	water cooler	water cooler	24	39	6	water cooler	otherfurniture	Furniture					n04559166	water_cooler.n.01	misc	40
+1174	cd case	cd case	24	40	7		otherprop	Objects							objects	39
+562	bowl	bowl	24	40	7	bowl	otherprop	Objects	bowl		bowl	2880940	n02880940	bowl.n.03	objects	39
+1175	closet rod	closet rod	24	40	7		otherprop	Objects					n04100174	rod.n.01	misc	40
+1156	bathroom counter	bathroom counter	24	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+84	oven	oven	23	38	7	oven	otherstructure	Objects					n03862676	oven.n.01	appliances	37
+104	stand	stand	23	39	6	stand	otherfurniture	Furniture	table	table	table	4379243	n04301000	stand.n.04	table	5
+229	scale	scale	23	40	7	scale	otherprop	Objects					n04141975	scale.n.07	objects	39
+70	washing machine	washing machine	23	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+325	broom	broom	22	40	7	broom	otherprop	Objects					n02906734	broom.n.01	objects	39
+169	hat	hat	22	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+128	shower wall	shower wall	22	1	12	wall	wall	Wall					n04208936	shower.n.01	wall	1
+331	guitar case	guitar case	21	40	7	guitar case	otherprop	Objects							objects	39
+87	rack	rack	21	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+488	water pitcher	water pitcher	21	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+776	laundry detergent	laundry detergent	21	40	7		otherprop	Objects							objects	39
+370	hair dryer	hair dryer	21	40	7	hair dryer	otherprop	Objects					n03483316	hand_blower.n.01	objects	39
+191	pillar	pillar	21	38	7	column	otherstructure	Objects					n03073977	column.n.07	column	24
+748	divider	divider	20	40	7		otherprop	Objects							wall	1
+242	power outlet	power outlet	19	40	7		otherprop	Objects							misc	40
+45	dining table	dining table	19	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+417	shower floor	shower floor	19	2	5	floor	floor	Floor					n04208936	shower.n.01	floor	2
+70	washing machines	washing machine	19	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+188	shower door	shower door	19	8	12	door	door	Wall	door				n04208936	shower.n.01	door	4
+1176	coffee kettle	coffee kettle	18	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1177	wardrobe cabinet	wardrobe 	18	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1178	structure	structure	18	38	7		otherstructure	Objects							misc	40
+18	bookshelves	bookshelf	17	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+110	clothes dryer	clothes dryer	17	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+148	toaster	toaster	17	40	7	toaster	otherprop	Objects					n04442312	toaster.n.02	appliances	37
+63	shoe	shoe	17	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+155	ironing board	ironing board	16	39	6	ironing board	otherfurniture	Furniture					n03586090	ironing_board.n.01	objects	39
+572	alarm clock	alarm clock	16	40	7	alarm clock	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+1179	shower head	shower head	15	38	7		otherstructure	Objects							shower	23
+28	lamp base	lamp	15	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+392	water bottle	water bottle	15	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n04557648	water_bottle.n.01	objects	39
+1180	keyboard piano	keyboard piano	15	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+609	projector screen	projector screen	15	38	7	projector screen	otherstructure	Objects							misc	40
+1181	case of water bottles	case of water bottles	15	40	7		otherprop	Objects							objects	39
+195	toaster oven	toaster oven	14	40	7	toaster oven	otherprop	Objects					n04442441	toaster_oven.n.01	appliances	37
+581	music stand	music stand	14	39	6	music stand	otherfurniture	Furniture					n03801760	music_stand.n.01	furniture	36
+58	staircase	stairs	14	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+1182	coat rack	coat rack	14	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	3
+1183	storage organizer	storage organizer	14	40	7		otherprop	Objects							shelving	3
+139	machine	machine	14	40	7	machine	otherprop	Objects					n03699975	machine.n.01	appliances	37
+1184	folded chair	folded chair	14	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1185	fire alarm	fire alarm	14	40	7		otherprop	Objects					n03343737	fire_alarm.n.02	misc	40
+156	fireplace	fireplace	13	38	7	fireplace	otherstructure	Objects					n03346455	fireplace.n.01	fireplace	27
+408	vent	vent	13	40	7		otherprop	Objects					n04526241	vent.n.01	misc	40
+213	furniture	furniture	13	39	6	furniture	otherfurniture	Furniture					n03405725	furniture.n.01	furniture	36
+1186	power strip	power strip	13	40	7		otherprop	Objects							objects	39
+1187	calendar	calendar	13	40	7		otherprop	Objects							objects	39
+1188	poster	poster	13	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+115	toilet paper holder	toilet paper holder	13	40	7	toilet paper holder	otherprop	Objects							objects	39
+1189	potted plant	potted plant	12	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+304	stuffed animal	stuffed animal	12	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+1190	luggage	luggage	12	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+21	curtains	curtain	12	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+312	headphones	headphones	12	40	7		otherprop	Objects					n03261776	earphone.n.01	objects	39
+233	crate	crate	12	39	6	crate	otherfurniture	Furniture					n03127925	crate.n.01	objects	39
+286	candle	candle	12	40	7	candle	otherprop	Objects	lamp				n02948072	candle.n.01	objects	39
+264	projector	projector	12	40	7	projector	otherprop	Objects					n04009552	projector.n.02	objects	39
+110	clothes dryers	clothes dryer	12	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+1191	mattress	mattress	12	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+356	dustpan	dustpan	12	40	7		otherprop	Objects					n03259009	dustpan.n.02	objects	39
+25	drawer	drawer	11	39	6	drawer	otherfurniture	Furniture					n03233905	drawer.n.01	furniture	36
+750	rod	rod	11	40	7		otherprop	Objects			pistol	3948459	n03427202	gat.n.01	misc	40
+269	globe	globe	11	40	7	globe	otherprop	Objects							objects	39
+307	footrest	footrest	11	39	6	foot rest	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+410	piano bench	piano bench	11	39	6	piano bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+730	breakfast bar	breakfast bar	11	38	7	bar	otherstructure	Objects							counter	26
+216	step stool	step stool	11	40	7	step stool	otherprop	Objects	stool				n04315713	step_stool.n.01	stool	19
+1192	hand rail	hand rail	11	38	7	railing	otherstructure	Objects							railing	30
+119	vending machine	vending machine	11	40	7	machine	otherprop	Objects					n04525305	vending_machine.n.01	appliances	37
+682	ceiling fan	ceiling fan	11	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+434	swiffer	swiffer	11	40	7		otherprop	Objects							objects	39
+126	foosball table	foosball table	11	39	6	foosball table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+919	jar	jar	11	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+85	footstool	footstool	11	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+1193	folded table	folded table	10	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+108	round table	round table	10	7	10	table	table	Table	table	table	table	4379243	n04114554	round_table.n.02	table	5
+135	hamper	hamper	10	40	7	basket	otherprop	Objects			basket	2801938	n03482405	hamper.n.02	objects	39
+1194	poster tube	poster tube	10	40	7		otherprop	Objects							objects	39
+432	case	case	10	40	7	case	otherprop	Objects							objects	39
+53	carpet	carpet	10	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+1195	thermostat	thermostat	10	40	7		otherprop	Objects					n04422875	thermostat.n.01	misc	40
+111	coat	coat	10	40	7	jacket	otherprop	Objects					n03057021	coat.n.01	clothes	38
+305	water fountain	water fountain	10	38	7	water fountain	otherstructure	Objects					n03241335	drinking_fountain.n.01	misc	40
+1125	smoke detector	smoke detector	10	40	7		otherprop	Objects							misc	40
+13	pillows	pillow	9	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+1196	flip flops	flip flops	9	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1197	cloth	cloth	9	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1198	banner	banner	9	40	7		otherprop	Objects					n02788021	banner.n.01	misc	40
+1199	clothes hanger	clothes hanger	9	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	objects	39
+1200	whiteboard eraser	whiteboard eraser	9	40	7		otherprop	Objects							objects	39
+378	iron	iron	9	40	7		otherprop	Objects					n03584829	iron.n.04	objects	39
+591	instrument case	instrument case	9	40	7	case	otherprop	Objects							objects	39
+49	toilet paper rolls	toilet paper	9	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+92	soap	soap	9	40	7	soap	otherprop	Objects					n04253437	soap.n.01	objects	39
+1098	block	block	9	40	7		otherprop	Objects							misc	40
+291	wall hanging	wall hanging	8	40	7		otherprop	Objects					n03491178	hanging.n.01	picture	6
+1063	kitchen island	kitchen island	8	38	7	kitchen island	otherstructure	Objects					n03620600	kitchen_island.n.01	counter	26
+107	pipes	pipe	8	38	7		otherstructure	Objects							misc	40
+1135	toothbrush	toothbrush	8	40	7	toothbrush	otherprop	Objects					n04453156	toothbrush.n.01	objects	39
+189	shirt	shirt	8	40	7		otherprop	Objects					n04197391	shirt.n.01	clothes	38
+245	cutting board	cutting board	8	40	7	cutting board	otherprop	Objects					n03025513	chopping_board.n.01	objects	39
+194	vase	vase	8	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1201	shower control valve	shower control valve	8	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+386	exercise machine	exercise machine	8	40	7	machine	otherprop	Objects							gym_equipment	33
+1202	compost bin	compost bin	8	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+857	shorts	shorts	8	40	7	shorts	otherprop	Objects							clothes	38
+452	tire	tire	8	40	7		otherprop	Objects					n04440749	tire.n.01	objects	39
+1203	teddy bear	teddy bear	7	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+346	bathrobe	bathrobe	7	40	7		otherprop	Objects					n02807616	bathrobe.n.01	clothes	38
+152	handrail	handrail	7	38	7	railing	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+83	faucet	faucet	7	40	7	faucet	otherprop	Objects			faucet	3325088	n03325088	faucet.n.01	misc	40
+1204	pantry wall	pantry wall	7	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+726	thermos	thermos	7	40	7	flask	otherprop	Objects	bottle		bottle	2876657	n04422727	thermos.n.01	objects	39
+61	rug	rug	7	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+39	couch cushions	cushion	7	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1117	tripod	tripod	7	39	6	stand	otherfurniture	Furniture					n04485082	tripod.n.01	objects	39
+540	mailbox	mailbox	7	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+1205	tupperware	tupperware	7	40	7		otherprop	Objects							objects	39
+415	shoe rack	shoe rack	7	40	7	shoe rack	otherprop	Objects							shelving	31
+31	towels	towel	6	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+1206	beer bottles	beer bottle	6	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+153	treadmill	treadmill	6	39	6	treadmill	otherfurniture	Furniture					n04477387	treadmill.n.01	gym_equipment	33
+1207	salt	salt	6	40	7		otherprop	Objects							objects	39
+129	chest	chest	6	39	6	chest	otherfurniture	Furniture	dresser	dresser					chest_of_drawers	13
+220	dispenser	dispenser	6	40	7		otherprop	Objects					n03210683	dispenser.n.01	objects	39
+1208	mirror doors	mirror door	6	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+231	remote	remote	6	40	7		otherprop	Objects			remote_control	4074963	n04074963	remote_control.n.01	objects	39
+1209	folded ladder	folded ladder	6	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	misc	40
+39	cushion	cushion	6	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1210	carton	carton	6	40	7		otherprop	Objects							objects	39
+117	step	step	6	38	7		otherstructure	Objects					n04314914	step.n.04	misc	40
+822	drying rack	drying rack	6	39	6	drying rack	otherfurniture	Furniture							shelving	31
+238	slippers	slipper	6	40	7	shoe	otherprop	Objects					n04241394	slipper.n.01	clothes	38
+143	pool table	pool table	6	39	6	pool table	otherfurniture	Furniture	table	table	table	4379243	n03982430	pool_table.n.01	table	5
+1211	soda stream	soda stream	6	40	7		otherprop	Objects							objects	39
+228	toilet brush	toilet brush	6	40	7	toilet brush	otherprop	Objects							objects	39
+494	loft bed	loft bed	6	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+226	cooking pot	cooking pot	6	40	7	pot	otherprop	Objects							objects	39
+91	heater	heater	6	39	6	heater	otherfurniture	Furniture					n03508101	heater.n.01	misc	40
+1072	messenger bag	messenger bag	6	37	7	bag	bag	Objects							objects	39
+435	stapler	stapler	6	40	7	stapler	otherprop	Objects					n04303497	stapler.n.01	objects	39
+1165	closet walls	closet wall	5	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+345	scanner	scanner	5	40	7		otherprop	Objects							appliances	37
+893	elliptical machine	elliptical machine	5	40	7	machine	otherprop	Objects							gym_equipment	33
+621	kettle	kettle	5	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1212	metronome	metronome	5	40	7		otherprop	Objects					n03757604	metronome.n.01	objects	39
+297	dumbell	dumbell	5	40	7		otherprop	Objects							objects	39
+1213	music book	music book	5	23	2	book	books	Books					n02870526	book.n.11	objects	39
+1214	rice cooker	rice cooker	5	40	7		otherprop	Objects							objects	39
+1215	dart board	dart board	5	38	7	board	otherstructure	Objects					n03162940	dartboard.n.01	objects	39
+529	sewing machine	sewing machine	5	40	7	sewing machine	otherprop	Objects					n04179913	sewing_machine.n.01	objects	39
+1216	grab bar	grab bar	5	38	7	railing	otherstructure	Objects							railing	30
+1217	flowerpot	flowerpot	5	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1218	painting	painting	5	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1219	railing	railing	5	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+1220	stair	stair	5	38	7	stairs	otherstructure	Objects	stairs				n04314914	step.n.04	stairs	16
+525	toolbox	toolbox	5	39	6	chest	otherfurniture	Furniture					n04452615	toolbox.n.01	objects	39
+204	nerf gun	nerf gun	5	40	7		otherprop	Objects							objects	39
+693	binders	binder	5	40	7	binder	otherprop	Objects							objects	39
+179	desk lamp	desk lamp	5	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1221	quadcopter	quadcopter	5	40	7		otherprop	Objects							objects	39
+1222	pitcher	pitcher	5	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+1223	hanging	hanging	5	40	7		otherprop	Objects							misc	40
+1224	mail	mail	5	40	7		otherprop	Objects							misc	40
+1225	closet ceiling	closet ceiling	5	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+1226	hoverboard	hoverboard	5	40	7		otherprop	Objects							objects	39
+1227	beanbag chair	beanbag chair	5	39	6	bean bag	otherfurniture	Furniture					n02816656	beanbag.n.01	chair	3
+571	water heater	water heater	5	40	7	water heater	otherprop	Objects					n04560113	water_heater.n.01	misc	40
+1228	spray bottle	spray bottle	5	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+556	rope	rope	5	40	7	rope	otherprop	Objects					n04108268	rope.n.01	objects	39
+280	plastic container	plastic container	5	40	7	container	otherprop	Objects							objects	39
+1229	soap bottle	soap bottle	5	40	7	soap	otherprop	Objects							objects	39
+1230	ikea bag	ikea bag	4	37	7	bag	bag	Objects				2773838	n02773838	bag.n.06	objects	39
+1231	sleeping bag	sleeping bag	4	40	7		otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+1232	duffel bag	duffel bag	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+746	frying pan	frying pan	4	40	7	frying pan	otherprop	Objects					n03400231	frying_pan.n.01	objects	39
+1233	oven mitt	oven mitt	4	40	7		otherprop	Objects							objects	39
+1234	pot	pot	4	40	7	pot	otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+144	hand dryer	hand dryer	4	40	7		otherprop	Objects							objects	39
+282	dollhouse	dollhouse	4	39	6	doll house	otherfurniture	Furniture					n03219483	dollhouse.n.01	objects	39
+167	shampoo bottle	shampoo bottle	4	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1235	hair brush	hair brush	4	40	7		otherprop	Objects					n02908217	brush.n.02	objects	39
+1236	tennis racket	tennis racket	4	40	7		otherprop	Objects					n04409806	tennis_racket.n.01	objects	39
+1237	display case	display case	4	40	7	case	otherprop	Objects							objects	39
+234	ping pong table	ping pong table	4	39	6	ping pong table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+563	boiler	boiler	4	40	7		otherprop	Objects							misc	40
+1238	bag of coffee beans	bag of coffee beans	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+1239	bananas	banana	4	40	7		otherprop	Objects					n00021265	food.n.01	objects	39
+1240	carseat	carseat	4	40	7		otherprop	Objects							misc	40
+366	helmet	helmet	4	40	7		otherprop	Objects			helmet	3513137	n03513137	helmet.n.02	clothes	38
+816	umbrella	umbrella	4	40	7	umbrella	otherprop	Objects					n04507155	umbrella.n.01	objects	39
+1241	coffee box	coffee box	4	40	7		otherprop	Objects							objects	39
+719	envelope	envelope	4	40	7	envelope	otherprop	Objects					n03291819	envelope.n.01	objects	39
+284	wet floor sign	wet floor sign	4	40	7	sign	otherprop	Objects							misc	40
+1242	clothing rack	clothing rack	4	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+247	controller	controller	4	40	7		otherprop	Objects					n03096960	control.n.09	objects	39
+1243	bath walls	bathroom wall	4	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+1244	podium	podium	4	39	6		otherfurniture	Furniture					n03159640	dais.n.01	furniture	36
+1245	storage box	storage box	4	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1246	dolly	dolly	4	40	7		otherprop	Objects							misc	40
+1247	shampoo	shampoo	3	40	7		otherprop	Objects					n04183516	shampoo.n.01	objects	39
+592	paper tray	paper tray	3	40	7	paper tray	otherprop	Objects							objects	39
+385	cabinet door	cabinet door	3	8	12	door	door	Wall	door						door	4
+1248	changing station	changing station	3	40	7		otherprop	Objects							misc	40
+1249	poster printer	poster printer	3	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+133	screen	screen	3	40	7		otherprop	Objects					n03151077	curtain.n.01	curtain	12
+301	soap bar	soap bar	3	38	7	bar	otherstructure	Objects							objects	39
+1250	crutches	crutches	3	40	7		otherprop	Objects					n03141823	crutch.n.01	objects	39
+379	studio light	studio light	3	38	7	light	otherstructure	Objects							lighting	28
+130	stack of cups	cup	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+1251	toilet flush button	toilet flush button	3	40	7		otherprop	Objects							objects	39
+450	trunk	trunk	3	40	7		otherprop	Objects							misc	40
+1252	grocery bag	grocery bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03461288	grocery_bag.n.01	objects	39
+316	plastic bin	plastic bin	3	40	7	bin	otherprop	Objects							objects	39
+1253	pizza box	pizza box	3	29	7	box	box	Objects							objects	39
+385	cabinet doors	cabinet door	3	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	door	4
+1254	legs	legs	3	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+461	car	car	3	40	7	car	otherprop	Objects	car		car	2958343	n02958343	car.n.01	misc	40
+1255	shaving cream	shaving cream	3	40	7		otherprop	Objects					n04186051	shaving_cream.n.01	objects	39
+1256	luggage stand	luggage stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+599	shredder	shredder	3	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+281	statue	statue	3	40	7	sculpture	otherprop	Objects					n04306847	statue.n.01	misc	40
+1257	urinal	urinal	3	33	7	toilet	toilet	Objects	toilet	toilet			n04515991	urinal.n.01	toilet	18
+1258	hose	hose	3	40	7		otherprop	Objects					n03539875	hose.n.03	misc	40
+1259	bike pump	bike pump	3	40	7		otherprop	Objects							objects	39
+319	coatrack	coatrack	3	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1260	bear	bear	3	40	7		otherprop	Objects							objects	39
+28	wall lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1261	humidifier	humidifier	3	40	7		otherprop	Objects							objects	39
+546	toothpaste	toothpaste	3	40	7	toothpaste	otherprop	Objects							objects	39
+1262	mouthwash bottle	mouthwash bottle	3	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1263	poster cutter	poster cutter	3	40	7		otherprop	Objects							objects	39
+1264	golf bag	golf bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03445617	golf_bag.n.01	objects	39
+1265	food container	food container	3	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1266	camera	camera	3	40	7		otherprop	Objects							objects	39
+28	table lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n04380533	table_lamp.n.01	lighting	28
+1267	yoga mat	yoga mat	3	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1268	card	card	3	40	7		otherprop	Objects							objects	39
+1269	mug	mug	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+188	shower doors	shower door	3	38	7		otherstructure	Objects					n04208936	shower.n.01	door	4
+689	cardboard	cardboard	3	40	7		otherprop	Objects							objects	39
+1270	rack stand	rack stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+1271	boxes of paper	boxes of paper	3	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1272	flag	flag	3	40	7		otherprop	Objects							misc	40
+354	futon	futon	3	39	6	mattress	otherfurniture	Furniture					n03408444	futon.n.01	sofa	10
+339	magazine	magazine	3	40	7	magazine	otherprop	Objects					n06595351	magazine.n.01	objects	39
+1009	exit sign	exit sign	3	40	7	exit sign	otherprop	Objects							misc	40
+1273	rolled poster	rolled poster	3	40	7		otherprop	Objects							objects	39
+1274	wheel	wheel	3	40	7		otherprop	Objects							objects	39
+15	pictures	picture	3	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1275	blackboard eraser	blackboard eraser	3	40	7	eraser	otherprop	Objects					n03294833	eraser.n.01	objects	39
+361	organizer	organizer	3	40	7		otherprop	Objects					n03918737	personal_digital_assistant.n.01	objects	39
+1276	doll	doll	3	40	7	toy	otherprop	Objects					n03219135	doll.n.01	objects	39
+326	book rack	book rack	3	39	6	bookrack	otherfurniture	Furniture							objects	39
+1277	laundry bag	laundry bag	3	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+1278	sponge	sponge	3	40	7		otherprop	Objects					n01906749	sponge.n.04	objects	39
+116	seating	seat	3	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+1184	folded chairs	folded chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1279	lotion bottle	lotion bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+212	can	can	2	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1280	lunch box	lunch box	2	40	7		otherprop	Objects							objects	39
+1281	food display	food display	2	40	7		otherprop	Objects							misc	40
+794	storage shelf	storage shelf	2	40	7		otherprop	Objects							shelving	31
+1282	sliding wood door	sliding wood door	2	40	7		otherprop	Objects							door	4
+955	pants	pants	2	40	7		otherprop	Objects					n04489008	trouser.n.01	clothes	38
+387	wood	wood	2	40	7		otherprop	Objects							misc	40
+69	boards	board	2	38	7	board	otherstructure	Objects							board_panel	35
+65	bottles	bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+523	washcloth	washcloth	2	40	7		otherprop	Objects					n04554523	washcloth.n.01	towel	20
+389	workbench	workbench	2	39	6	bench	otherfurniture	Furniture	bench		table	4379243	n04600486	workbench.n.01	table	5
+29	open kitchen cabinet	kitchen cabinet	2	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+1283	organizer shelf	organizer shelf	2	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+146	frame	frame	2	38	7		otherstructure	Objects							misc	40
+130	cups	cup	2	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+372	exercise ball	exercise ball	2	40	7	ball	otherprop	Objects					n04285146	sports_equipment.n.01	gym_equipment	33
+289	easel	easel	2	39	6	stand	otherfurniture	Furniture					n03262809	easel.n.01	furniture	36
+440	garbage bag	garbage bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+321	roomba	roomba	2	40	7		otherprop	Objects							objects	39
+976	garage door	garage door	2	38	7	garage door	otherstructure	Objects	door						door	4
+1256	luggage rack	luggage stand	2	39	6	stand	otherfurniture	Furniture					n04038440		shelving	31
+1284	bike lock	bike lock	2	40	7		otherprop	Objects							objects	39
+1285	briefcase	briefcase	2	40	7		otherprop	Objects					n02900705	briefcase.n.01	objects	39
+357	hand towel	hand towel	2	27	7	towel	towel	Objects					n03490006	hand_towel.n.01	towel	20
+1286	bath products	bath product	2	40	7		otherprop	Objects							objects	39
+1287	star	star	2	40	7		otherprop	Objects					n09444783	star.n.03	misc	40
+365	map	map	2	40	7	map	otherprop	Objects					n03720163	map.n.01	misc	40
+1288	coffee bean bag	coffee bean bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+81	headboard	headboard	2	39	6	headboard	otherfurniture	Furniture					n03502200	headboard.n.01	bed	11
+1289	ipad	ipad	2	40	7		otherprop	Objects							objects	39
+1290	display rack	display rack	2	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+948	traffic cone	traffic cone	2	40	7	cone	otherprop	Objects	cone						objects	39
+174	toiletry	toiletry	2	40	7		otherprop	Objects					n04447443	toiletry.n.01	objects	39
+1028	canopy	canopy	2	40	7		otherprop	Objects							misc	40
+1291	massage chair	massage chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1292	paper organizer	paper organizer	2	40	7		otherprop	Objects							objects	39
+1005	barricade	barricade	2	40	7		otherprop	Objects							misc	40
+235	platform	platform	2	38	7		otherstructure	Objects							misc	40
+1293	cap	cap	2	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+1294	dumbbell plates	dumbbell plates	2	40	7		otherprop	Objects							objects	39
+1295	elevator	elevator	2	38	7		otherstructure	Objects							misc	40
+1296	cooking pan	cooking pan	2	40	7	pan	otherprop	Objects					n03880531	pan.n.01	objects	39
+1297	trash bag	trash bag	2	37	7	bag	bag	Objects							objects	39
+1298	santa	santa	2	40	7		otherprop	Objects							misc	40
+1299	jewelry box	jewelry box	2	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1300	boat	boat	2	40	7		otherprop	Objects							misc	40
+1301	sock	sock	2	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1051	kinect	kinect	2	40	7	kinect	otherprop	Objects							objects	39
+566	crib	crib	2	39	6	crib	otherfurniture	Furniture							furniture	36
+1302	plastic storage bin	plastic storage bin	2	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1062	cooler	cooler	2	24	6	refridgerator	refridgerator	Furniture					n03102654	cooler.n.01	appliances	37
+1303	kitchen apron	kitchen apron	2	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1304	dishwashing soap bottle	dishwashing soap bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1305	xbox controller	xbox controller	2	40	7		otherprop	Objects							objects	39
+1306	banana holder	banana holder	2	40	7		otherprop	Objects							objects	39
+298	ping pong paddle	ping pong paddle	2	40	7		otherprop	Objects							table	5
+1307	airplane	airplane	2	40	7		otherprop	Objects							misc	40
+1308	conditioner bottle	conditioner bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1309	tea kettle	tea kettle	2	40	7	tea kettle	otherprop	Objects					n04397768	teakettle.n.01	objects	39
+43	bedframe	bedframe	2	39	6		otherfurniture	Furniture					n02822579	bedstead.n.01	bed	11
+1310	wood beam	wood beam	2	38	7		otherstructure	Objects							beam	29
+593	toilet paper package	toilet paper package	2	40	7		otherprop	Objects							objects	39
+1311	wall mounted coat rack	wall mounted coat rack	2	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1312	film light	film light	2	40	7		otherprop	Objects							lighting	28
+749	ceiling lamp	ceiling lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+623	chain	chain	1	40	7		otherprop	Objects							chair	3
+1313	sofa	sofa	1	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+99	closet wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+265	sweater	sweater	1	40	7		otherprop	Objects					n04370048	sweater.n.01	clothes	38
+1314	kitchen mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+99	wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1315	water softener	water softener	1	40	7		otherprop	Objects							misc	40
+448	banister	banister	1	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+257	trolley	trolley	1	40	7	trolley	otherprop	Objects					n04335435	streetcar.n.01	misc	40
+1316	pantry shelf	pantry shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+786	sofa bed	sofa bed	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+801	loofa	loofa	1	40	7		otherprop	Objects							objects	39
+972	shower faucet handle	shower faucet handle	1	40	7	handle	otherprop	Objects							shower	23
+1317	toy piano	toy piano	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1318	fish	fish	1	40	7		otherprop	Objects					n02512053	fish.n.01	objects	39
+75	file cabinets	file cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n03337140	file.n.03	cabinet	7
+657	cat litter box	cat litter box	1	29	7	box	box	Objects							objects	39
+561	electric panel	electric panel	1	40	7		otherprop	Objects							misc	40
+93	suitcases	suitcase	1	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+513	curtain rod	curtain rod	1	38	7	curtain rod	otherstructure	Objects							curtain	12
+411	bunk bed	bunk bed	1	39	6	bunk bed	otherfurniture	Furniture	bed	bed	bed	2818832	n02920259	bunk_bed.n.01	bed	11
+1122	chandelier	chandelier	1	38	7	chandelier	otherstructure	Objects					n03005285	chandelier.n.01	lighting	28
+922	tape	tape	1	40	7	tape	otherprop	Objects							objects	39
+88	plates	plate	1	40	7		otherprop	Objects					n03959485	plate.n.04	objects	39
+518	alarm	alarm	1	40	7	alarm	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+814	fire hose	fire hose	1	40	7		otherprop	Objects					n03346004	fire_hose.n.01	misc	40
+1319	toy dinosaur	toy dinosaur	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1320	cone	cone	1	40	7		otherprop	Objects							objects	39
+649	glass doors	glass door	1	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+607	hatrack	hatrack	1	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+819	subwoofer	subwoofer	1	40	7	speaker	otherprop	Objects			speaker	3691459	n04349401	subwoofer.n.01	objects	39
+1321	fire sprinkler	fire sprinkler	1	40	7		otherprop	Objects							misc	40
+1322	trash cabinet	trash cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+1204	pantry walls	pantry wall	1	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+227	photo	photo	1	40	7	photo	otherprop	Objects					n03925226	photograph.n.01	picture	6
+817	barrier	barrier	1	40	7		otherprop	Objects					n02796623	barrier.n.01	misc	40
+130	stacks of cups	cup	1	40	7		otherprop	Objects					n03147509	cup.n.01	objects	39
+712	beachball	beachball	1	40	7	ball	otherprop	Objects					n02814224	beach_ball.n.01	objects	39
+1323	folded boxes	folded boxes	1	40	7		otherprop	Objects							objects	39
+1324	contact lens solution bottle	contact lens solution bottle	1	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+673	covered box	covered box	1	29	7	box	box	Objects							objects	39
+459	folder	folder	1	40	7	folder	otherprop	Objects					n03376279	folder.n.02	objects	39
+643	mail trays	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+238	slipper	slipper	1	40	7		otherprop	Objects					n04241394	slipper.n.01	clothes	38
+765	magazine rack	magazine rack	1	39	6	stand	otherfurniture	Furniture					n03704549	magazine_rack.n.01	shelving	31
+1008	sticker	sticker	1	40	7	sticker	otherprop	Objects					n07272545	gummed_label.n.01	objects	39
+225	lotion	lotion	1	40	7		otherprop	Objects					n03690938	lotion.n.01	objects	39
+1083	buddha	buddha	1	40	7		otherprop	Objects							objects	39
+813	file organizer	file organizer	1	40	7		otherprop	Objects							objects	39
+138	paper towel rolls	paper towel roll	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+1145	night lamp	night lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+796	fuse box	fuse box	1	40	7		otherprop	Objects							misc	40
+1325	knife block	knife block	1	40	7		otherprop	Objects							objects	39
+363	furnace	furnace	1	39	6	furnace	otherfurniture	Furniture					n03404449	furnace.n.01		
+1174	cd cases	cd case	1	40	7		otherprop	Objects							objects	39
+38	stools	stool	1	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+1326	hand sanitzer dispenser	hand sanitzer dispenser	1	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+997	teapot	teapot	1	40	7	tea pot	otherprop	Objects					n04398044	teapot.n.01	objects	39
+1327	pen holder	pen holder	1	40	7		otherprop	Objects							objects	39
+1328	tray rack	tray rack	1	40	7		otherprop	Objects							objects	39
+1329	wig	wig	1	40	7		otherprop	Objects					n04584207	wig.n.01	objects	39
+182	switch	switch	1	40	7		otherprop	Objects					n04372370	switch.n.01	misc	40
+280	plastic containers	plastic container	1	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1330	night light	night light	1	40	7		otherprop	Objects							lighting	28
+1331	notepad	notepad	1	40	7		otherprop	Objects							objects	39
+1332	mail bin	mail bin	1	40	7		otherprop	Objects							misc	40
+1333	elevator button	elevator button	1	40	7		otherprop	Objects							misc	40
+939	gaming wheel	gaming wheel	1	40	7		otherprop	Objects							objects	39
+1334	drum set	drum set	1	40	7		otherprop	Objects							objects	39
+480	cosmetic bag	cosmetic bag	1	37	7	bag	bag	Objects							objects	39
+907	coffee mug	coffee mug	1	40	7	vessel	otherprop	Objects			cup or mug	3797390	n03063599	coffee_mug.n.01	objects	39
+1335	closet shelf	closet shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+1336	baby mobile	baby mobile	1	40	7		otherprop	Objects							objects	39
+829	diaper bin	diaper bin	1	40	7	bin	otherprop	Objects							objects	39
+947	door wall	door wall	1	1	12	wall	wall	Wall							wall	1
+1116	stepstool	stepstool	1	40	7	step stool	otherprop	Objects							objects	39
+599	paper shredder	shredder	1	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+733	dress rack	dress rack	1	40	7		otherprop	Objects					n03238762	dress_rack.n.01	misc	40
+123	cover	cover	1	40	7	blanket	otherprop	Objects							objects	39
+506	shopping bag	shopping bag	1	37	7	bag	bag	Objects					n04204081	shopping_bag.n.01	objects	39
+569	sliding door	sliding door	1	8	12	door	door	Wall	door				n04239074	sliding_door.n.01	door	4
+1337	exercise bike	exercise bike	1	40	7	machine	otherprop	Objects					n04210120	shredder.n.01	gym_equipment	33
+1338	recliner chair	recliner chair	1	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03238762	dress_rack.n.01	chair	3
+1314	kitchenaid mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+1339	soda can	soda can	1	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1340	stovetop	stovetop	1	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+851	stepladder	stepladder	1	39	6	ladder	otherfurniture	Furniture	stairs				n04315599	step_ladder.n.01	stairs	16
+142	tap	tap	1	40	7	faucet	otherprop	Objects			faucet	3325088	n04559451	water_faucet.n.01	objects	39
+436	cable	cable	1	40	7	cables	otherprop	Objects							objects	39
+1341	baby changing station	baby changing station	1	39	6		otherfurniture	Furniture							furniture	36
+1342	costume	costume	1	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+885	rocking chair	rocking chair	1	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04099969	rocking_chair.n.01	chair	3
+693	binder	binder	1	40	7	binder	otherprop	Objects							objects	39
+815	media center	media center	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+401	towel rack	towel rack	1	40	7		otherprop	Objects					n04459773	towel_rack.n.01	misc	40
+1343	medal	medal	1	40	7		otherprop	Objects							objects	39
+1184	stack of folded chairs	folded chair	1	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1344	telescope	telescope	1	40	7		otherprop	Objects					n04403638	telescope.n.01	objects	39
+1345	closet doorframe	closet doorframe	1	8	12	door	door	Wall	door						door	4
+160	glass	glass	1	38	7	glass	otherstructure	Objects					n03438257	glass.n.02	misc	40
+1126	baseball cap	baseball cap	1	40	7		otherprop	Objects			cap	2954340	n02799323	baseball_cap.n.01	clothes	38
+1346	battery disposal jar	battery disposal jar	1	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+332	mop	mop	1	40	7		otherprop	Objects					n04367480	swab.n.02	objects	39
+397	tank	tank	1	40	7		otherprop	Objects							objects	39
+643	mail tray	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+551	centerpiece	centerpiece	1	40	7	centerpiece	otherprop	Objects					n02994419	centerpiece.n.02	objects	39
+1163	stick	stick	1	40	7	stick	otherprop	Objects							objects	39
+1347	closet floor	closet floor	1	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+1348	dryer sheets	dryer sheets	1	40	7		otherprop	Objects							objects	39
+803	bycicle	bycicle	1	40	7		otherprop	Objects							misc	40
+484	flower stand	flower stand	1	39	6	stand	otherfurniture	Furniture							furniture	36
+1349	air mattress	air mattress	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02690809	air_mattress.n.01	bed	11
+1350	clip	clip	1	40	7		otherprop	Objects							objects	39
+222	side table	side table	1	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+1253	pizza boxes	pizza box	1	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1351	display	display	1	39	7		otherfurniture	Furniture					n03211117	display.n.06	misc	40
+1352	postcard	postcard	1	40	7		otherprop	Objects							objects	39
+828	display sign	display sign	1	40	7	sign	otherprop	Objects							misc	40
+1353	paper towel	paper towel	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+612	boots	boot	1	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1354	tennis racket bag	tennis racket bag	1	40	7		otherprop	Objects							objects	39
+1355	air hockey table	air hockey table	1	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+1301	socks	sock	1	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1356	food bag	food bag	1	37	7	bag	bag	Objects							objects	39
+1199	clothes hangers	clothes hanger	1	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	misc	40
+1357	starbucks cup	starbucks cup	1	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv

@@ -0,0 +1,608 @@
+id	raw_category	category	count	nyu40id	eigen13id	nyuClass	nyu40class	eigen13class	ModelNet40	ModelNet10	ShapeNetCore55	synsetoffset	wnsynsetid	wnsynsetkey	mpcat40	mpcat40index
+1	wall	wall	8277	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+2	chair	chair	4646	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+22	books	book	1678	23	2	book	books	Books					n02870526	book.n.11	objects	39
+3	floor	floor	1553	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+5	door	door	1483	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+1163	object	object	1313	40	7		otherprop	Objects							objects	39
+16	window	window	1209	9	13	window	window	Window					n04587648	window.n.01	window	9
+4	table	table	1170	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+56	trash can	trash can	1090	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+13	pillow	pillow	937	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+15	picture	picture	862	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+41	ceiling	ceiling	806	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+26	box	box	775	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+161	doorframe	doorframe	768	8	12	door	door	Wall	door					doorframe.n.01	door	4
+19	monitor	monitor	765	40	7	monitor	otherprop	Objects	monitor	monitor	tv or monitor	3211117	n03782190	monitor.n.04	objects	39
+7	cabinet	cabinet	731	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+9	desk	desk	680	14	10	desk	desk	Table	desk	desk	table	4379243	n03179701	desk.n.01	table	5
+8	shelf	shelf	641	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+10	office chair	office chair	595	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04373704	swivel_chair.n.01	chair	3
+31	towel	towel	570	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+6	couch	couch	502	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+14	sink	sink	488	34	7	sink	sink	Objects	sink				n04223580	sink.n.01	sink	15
+48	backpack	backpack	479	40	7	backpack	otherprop	Objects					n02769748	backpack.n.01	objects	39
+28	lamp	lamp	419	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+11	bed	bed	370	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+18	bookshelf	bookshelf	360	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+71	mirror	mirror	349	19	7	mirror	mirror	Objects					n03773035	mirror.n.01	mirror	21
+21	curtain	curtain	347	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+40	plant	plant	331	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+52	whiteboard	whiteboard	327	30	7	whiteboard	whiteboard	Objects					n03211616	display_panel.n.01	board_panel	35
+96	radiator	radiator	322	39	6	radiator	otherfurniture	Furniture					n04041069	radiator.n.02	misc	40
+22	book	book	318	23	2	book	books	Books					n02870526	book.n.11	objects	39
+29	kitchen cabinet	kitchen cabinet	310	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+49	toilet paper	toilet paper	291	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+29	kitchen cabinets	kitchen cabinet	289	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+23	armchair	armchair	281	5	4	chair	chair	Chair	chair	chair	chair	3001627	n02738535	armchair.n.01	chair	3
+63	shoes	shoe	272	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+24	coffee table	coffee table	258	7	10	coffee table	table	Table	table	table	table	4379243	n03063968	coffee_table.n.01	table	5
+17	toilet	toilet	256	33	7	toilet	toilet	Objects	toilet	toilet			n04446276	toilet.n.01	toilet	18
+47	bag	bag	252	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+32	clothes	clothes	248	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+46	keyboard	keyboard	246	40	7	keyboard	otherprop	Objects	keyboard		computer keyboard	3085013	n03085013	computer_keyboard.n.01	objects	39
+65	bottle	bottle	226	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+97	recycling bin	recycling bin	225	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+34	nightstand	nightstand	224	32	6	night stand	night stand	Furniture	night_stand	night_stand			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+38	stool	stool	221	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+33	tv	tv	219	25	11	television	television	TV			tv or monitor	3211117	n03211117	display.n.06	tv_monitor	22
+75	file cabinet	file cabinet	217	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+36	dresser	dresser	213	17	6	dresser	dresser	Furniture	dresser	dresser			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+64	computer tower	computer tower	203	40	7	computer	otherprop	Objects					n03082979	computer.n.01	objects	39
+32	clothing	clothes	165	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+101	telephone	telephone	164	40	7	telephone	otherprop	Objects			telephone	4401088	n04401088	telephone.n.01	objects	39
+130	cup	cup	157	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+27	refrigerator	refrigerator	154	24	6	refridgerator	refridgerator	Furniture					n04070727	refrigerator.n.01	appliances	37
+44	end table	end table	147	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+131	jacket	jacket	146	40	7	jacket	otherprop	Objects					n03589791	jacket.n.01	clothes	38
+55	shower curtain	shower curtain	144	28	7	shower curtain	shower curtain	Objects	curtain				n04209239	shower_curtain.n.01	curtain	12
+42	bathtub	bathtub	144	36	7	bathtub	bathtub	Objects	bathtub	bathtub	tub	2808440	n02808440	bathtub.n.01	bathtub	25
+59	microwave	microwave	141	40	7	microwave	otherprop	Objects			microwave	3761084	n03761084	microwave.n.02	appliances	37
+159	kitchen counter	kitchen counter	140	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+74	sofa chair	sofa chair	129	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+82	paper towel dispenser	paper towel dispenser	129	40	7	paper towel dispenser	otherprop	Objects							objects	39
+1164	bathroom vanity	bathroom vanity	126	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	table	5
+93	suitcase	suitcase	118	40	7	luggage	otherprop	Objects					n02773838	bag.n.06	objects	39
+77	laptop	laptop	111	40	7	laptop	otherprop	Objects	laptop		laptop	3642806	n03642806	laptop.n.01	objects	39
+67	ottoman	ottoman	111	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+128	shower walls	shower wall	109	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+50	printer	printer	106	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+35	counter	counter	104	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+69	board	board	100	38	7	board	otherstructure	Objects							board_panel	35
+100	soap dispenser	soap dispenser	99	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+62	stove	stove	95	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+105	light	light	93	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+1165	closet wall	closet wall	90	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+165	mini fridge	mini fridge	87	24	6	refridgerator	refridgerator	Furniture					n03273913	electric_refrigerator.n.01	appliances	37
+7	cabinets	cabinet	79	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+5	doors	door	76	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+76	fan	fan	75	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+230	tissue box	tissue box	73	40	7	tissue box	otherprop	Objects					n02883344	box.n.01	objects	39
+54	blanket	blanket	72	40	7	blanket	otherprop	Objects					n02849154	blanket.n.01	objects	39
+125	bathroom stall	bathroom stall	71	38	7		otherstructure	Objects					n02873839	booth.n.02	misc	40
+72	copier	copier	70	40	7		otherprop	Objects					n03257586	duplicator.n.01	appliances	37
+68	bench	bench	66	39	6	bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+145	bar	bar	66	38	7	bar	otherstructure	Objects					n02788689	bar.n.03	misc	40
+157	soap dish	soap dish	65	40	7	soap dish	otherprop	Objects					n04254009	soap_dish.n.01	objects	39
+1166	laundry hamper	laundry hamper	65	40	7	laundry basket	otherprop	Objects							objects	39
+132	storage bin	storage bin	63	40	7	storage bin	otherprop	Objects							objects	39
+1167	bathroom stall door	bathroom stall door	62	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+232	light switch	light switch	61	38	7	light switch	otherstructure	Objects					n04372370	switch.n.01	misc	40
+134	coffee maker	coffee maker	61	40	7		otherprop	Objects					n03063338	coffee_maker.n.01	appliances	37
+51	tv stand	tv stand	61	39	6	tv stand	otherfurniture	Furniture	tv_stand				n03290653	entertainment_center.n.01	furniture	36
+250	decoration	decoration	60	40	7		otherprop	Objects					n03169390	decoration.n.01	misc	40
+1168	ceiling light	ceiling light	59	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+342	range hood	range hood	59	38	7	range hood	otherstructure	Objects	range_hood				n04053677	range_hood.n.01	misc	40
+89	blackboard	blackboard	58	38	7	blackboard	otherstructure	Objects					n02846511	blackboard.n.01	board_panel	35
+103	clock	clock	58	40	7	clock	otherprop	Objects			clock	3046257	n03046257	clock.n.01	objects	39
+99	wardrobe closet	wardrobe	54	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+95	rail	rail	53	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+154	bulletin board	bulletin board	53	38	7	board	otherstructure	Objects					n03211616	display_panel.n.01	board_panel	35
+140	mat	mat	52	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1169	trash bin	trash bin	52	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+193	ledge	ledge	51	38	7		otherstructure	Objects					n09337253	ledge.n.01	misc	40
+116	seat	seat	49	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+202	mouse	mouse	49	40	7	mouse	otherprop	Objects					n03793489	mouse.n.04	objects	39
+73	basket	basket	48	40	7	basket	otherprop	Objects			basket	2801938	n02801938	basket.n.01	objects	39
+78	shower	shower	48	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+1170	dumbbell	dumbbell	48	40	7		otherprop	Objects					n03255030	dumbbell.n.01	objects	39
+79	paper	paper	46	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+80	person	person	46	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+141	windowsill	windowsill	45	38	7		otherstructure	Objects					n04590263	windowsill.n.01	window	9
+57	closet	closet	45	39	6	wardrobe	otherfurniture	Furniture	wardrobe						misc	40
+102	bucket	bucket	45	40	7	bucket	otherprop	Objects					n02909870	bucket.n.01	misc	40
+261	sign	sign	44	40	7	sign	otherprop	Objects					n04217882	signboard.n.01	objects	39
+118	speaker	speaker	43	40	7	speaker	otherprop	Objects			speaker	3691459	n03691459	loudspeaker.n.01	objects	39
+136	dishwasher	dishwasher	43	38	7	dishwasher	otherstructure	Objects			dishwasher	3207941	n03207941	dishwasher.n.01	appliances	37
+98	container	container	43	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1171	stair rail	stair rail	42	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+170	shower curtain rod	shower curtain rod	42	40	7		otherprop	Objects							curtain	12
+1172	tube	tube	41	40	7		otherprop	Objects							misc	40
+1173	bathroom cabinet	bathroom cabinet	39	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+79	papers	paper	39	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+221	storage container	storage container	39	40	7	container	otherprop	Objects							objects	39
+570	paper bag	paper bag	39	37	7	bag	bag	Objects					n04122825	sack.n.01	objects	39
+138	paper towel roll	paper towel roll	39	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+168	ball	ball	39	40	7	ball	otherprop	Objects							objects	39
+276	closet doors	closet door	38	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+106	laundry basket	laundry basket	37	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+214	cart	cart	37	40	7	cart	otherprop	Objects					n03484083	handcart.n.01	shelving	31
+276	closet door	closet door	35	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+323	dish rack	dish rack	35	40	7	dish rack	otherprop	Objects					n03207630	dish_rack.n.01	objects	39
+58	stairs	stairs	35	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+86	blinds	blinds	35	13	13	blinds	blinds	Window					n02851099	blind.n.03	blinds	32
+2	stack of chairs	chair	35	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+399	purse	purse	34	40	7	purse	otherprop	Objects					n02774152	bag.n.04	objects	39
+121	bicycle	bicycle	33	40	7	bicycle	otherprop	Objects			bicycle	2834778	n02834778	bicycle.n.01	objects	39
+185	tray	tray	32	40	7	tray	otherprop	Objects					n04476259	tray.n.01	objects	39
+300	plunger	plunger	30	40	7		otherprop	Objects					n03970156	plunger.n.03	objects	39
+180	paper cutter	paper cutter	30	40	7	paper cutter	otherprop	Objects					n03886940	paper_cutter.n.01	objects	39
+163	toilet paper dispenser	toilet paper dispenser	29	40	7		otherprop	Objects							objects	39
+26	boxes	box	29	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+66	bin	bin	28	40	7	bin	otherprop	Objects					n02839910	bin.n.01	objects	39
+208	toilet seat cover dispenser	toilet seat cover dispenser	28	40	7		otherprop	Objects							objects	39
+112	guitar	guitar	28	40	7	guitar	otherprop	Objects	guitar		guitar	3467517	n03467517	guitar.n.01	objects	39
+540	mailboxes	mailbox	28	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+395	handicap bar	handicap bar	27	38	7	bar	otherstructure	Objects							misc	40
+166	fire extinguisher	fire extinguisher	27	40	7	fire extinguisher	otherprop	Objects					n03345837	fire_extinguisher.n.01	misc	40
+122	ladder	ladder	27	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	stairs	16
+120	column	column	26	38	7	column	otherstructure	Objects					n03074380	column.n.06	column	24
+107	pipe	pipe	25	40	7	pipe	otherprop	Objects					n03944672	pipe.n.02	misc	40
+283	vacuum cleaner	vacuum cleaner	25	40	7		otherprop	Objects					n04517823	vacuum.n.04	objects	39
+88	plate	plate	24	40	7	plate	otherprop	Objects					n03959485	plate.n.04	objects	39
+90	piano	piano	24	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+177	water cooler	water cooler	24	39	6	water cooler	otherfurniture	Furniture					n04559166	water_cooler.n.01	misc	40
+1174	cd case	cd case	24	40	7		otherprop	Objects							objects	39
+562	bowl	bowl	24	40	7	bowl	otherprop	Objects	bowl		bowl	2880940	n02880940	bowl.n.03	objects	39
+1175	closet rod	closet rod	24	40	7		otherprop	Objects					n04100174	rod.n.01	misc	40
+1156	bathroom counter	bathroom counter	24	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+84	oven	oven	23	38	7	oven	otherstructure	Objects					n03862676	oven.n.01	appliances	37
+104	stand	stand	23	39	6	stand	otherfurniture	Furniture	table	table	table	4379243	n04301000	stand.n.04	table	5
+229	scale	scale	23	40	7	scale	otherprop	Objects					n04141975	scale.n.07	objects	39
+70	washing machine	washing machine	23	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+325	broom	broom	22	40	7	broom	otherprop	Objects					n02906734	broom.n.01	objects	39
+169	hat	hat	22	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+128	shower wall	shower wall	22	1	12	wall	wall	Wall					n04208936	shower.n.01	wall	1
+331	guitar case	guitar case	21	40	7	guitar case	otherprop	Objects							objects	39
+87	rack	rack	21	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+488	water pitcher	water pitcher	21	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+776	laundry detergent	laundry detergent	21	40	7		otherprop	Objects							objects	39
+370	hair dryer	hair dryer	21	40	7	hair dryer	otherprop	Objects					n03483316	hand_blower.n.01	objects	39
+191	pillar	pillar	21	38	7	column	otherstructure	Objects					n03073977	column.n.07	column	24
+748	divider	divider	20	40	7		otherprop	Objects							wall	1
+242	power outlet	power outlet	19	40	7		otherprop	Objects							misc	40
+45	dining table	dining table	19	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+417	shower floor	shower floor	19	2	5	floor	floor	Floor					n04208936	shower.n.01	floor	2
+70	washing machines	washing machine	19	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+188	shower door	shower door	19	8	12	door	door	Wall	door				n04208936	shower.n.01	door	4
+1176	coffee kettle	coffee kettle	18	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1177	wardrobe cabinet	wardrobe	18	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1178	structure	structure	18	38	7		otherstructure	Objects							misc	40
+18	bookshelves	bookshelf	17	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+110	clothes dryer	clothes dryer	17	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+148	toaster	toaster	17	40	7	toaster	otherprop	Objects					n04442312	toaster.n.02	appliances	37
+63	shoe	shoe	17	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+155	ironing board	ironing board	16	39	6	ironing board	otherfurniture	Furniture					n03586090	ironing_board.n.01	objects	39
+572	alarm clock	alarm clock	16	40	7	alarm clock	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+1179	shower head	shower head	15	38	7		otherstructure	Objects							shower	23
+28	lamp base	lamp	15	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+392	water bottle	water bottle	15	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n04557648	water_bottle.n.01	objects	39
+1180	keyboard piano	keyboard piano	15	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+609	projector screen	projector screen	15	38	7	projector screen	otherstructure	Objects							misc	40
+1181	case of water bottles	case of water bottles	15	40	7		otherprop	Objects							objects	39
+195	toaster oven	toaster oven	14	40	7	toaster oven	otherprop	Objects					n04442441	toaster_oven.n.01	appliances	37
+581	music stand	music stand	14	39	6	music stand	otherfurniture	Furniture					n03801760	music_stand.n.01	furniture	36
+58	staircase	stairs	14	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+1182	coat rack	coat rack	14	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	3
+1183	storage organizer	storage organizer	14	40	7		otherprop	Objects							shelving	3
+139	machine	machine	14	40	7	machine	otherprop	Objects					n03699975	machine.n.01	appliances	37
+1184	folded chair	folded chair	14	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1185	fire alarm	fire alarm	14	40	7		otherprop	Objects					n03343737	fire_alarm.n.02	misc	40
+156	fireplace	fireplace	13	38	7	fireplace	otherstructure	Objects					n03346455	fireplace.n.01	fireplace	27
+408	vent	vent	13	40	7		otherprop	Objects					n04526241	vent.n.01	misc	40
+213	furniture	furniture	13	39	6	furniture	otherfurniture	Furniture					n03405725	furniture.n.01	furniture	36
+1186	power strip	power strip	13	40	7		otherprop	Objects							objects	39
+1187	calendar	calendar	13	40	7		otherprop	Objects							objects	39
+1188	poster	poster	13	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+115	toilet paper holder	toilet paper holder	13	40	7	toilet paper holder	otherprop	Objects							objects	39
+1189	potted plant	potted plant	12	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+304	stuffed animal	stuffed animal	12	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+1190	luggage	luggage	12	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+21	curtains	curtain	12	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+312	headphones	headphones	12	40	7		otherprop	Objects					n03261776	earphone.n.01	objects	39
+233	crate	crate	12	39	6	crate	otherfurniture	Furniture					n03127925	crate.n.01	objects	39
+286	candle	candle	12	40	7	candle	otherprop	Objects	lamp				n02948072	candle.n.01	objects	39
+264	projector	projector	12	40	7	projector	otherprop	Objects					n04009552	projector.n.02	objects	39
+110	clothes dryers	clothes dryer	12	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+1191	mattress	mattress	12	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+356	dustpan	dustpan	12	40	7		otherprop	Objects					n03259009	dustpan.n.02	objects	39
+25	drawer	drawer	11	39	6	drawer	otherfurniture	Furniture					n03233905	drawer.n.01	furniture	36
+750	rod	rod	11	40	7		otherprop	Objects			pistol	3948459	n03427202	gat.n.01	misc	40
+269	globe	globe	11	40	7	globe	otherprop	Objects							objects	39
+307	footrest	footrest	11	39	6	foot rest	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+410	piano bench	piano bench	11	39	6	piano bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+730	breakfast bar	breakfast bar	11	38	7	bar	otherstructure	Objects							counter	26
+216	step stool	step stool	11	40	7	step stool	otherprop	Objects	stool				n04315713	step_stool.n.01	stool	19
+1192	hand rail	hand rail	11	38	7	railing	otherstructure	Objects							railing	30
+119	vending machine	vending machine	11	40	7	machine	otherprop	Objects					n04525305	vending_machine.n.01	appliances	37
+682	ceiling fan	ceiling fan	11	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+434	swiffer	swiffer	11	40	7		otherprop	Objects							objects	39
+126	foosball table	foosball table	11	39	6	foosball table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+919	jar	jar	11	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+85	footstool	footstool	11	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+1193	folded table	folded table	10	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+108	round table	round table	10	7	10	table	table	Table	table	table	table	4379243	n04114554	round_table.n.02	table	5
+135	hamper	hamper	10	40	7	basket	otherprop	Objects			basket	2801938	n03482405	hamper.n.02	objects	39
+1194	poster tube	poster tube	10	40	7		otherprop	Objects							objects	39
+432	case	case	10	40	7	case	otherprop	Objects							objects	39
+53	carpet	carpet	10	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+1195	thermostat	thermostat	10	40	7		otherprop	Objects					n04422875	thermostat.n.01	misc	40
+111	coat	coat	10	40	7	jacket	otherprop	Objects					n03057021	coat.n.01	clothes	38
+305	water fountain	water fountain	10	38	7	water fountain	otherstructure	Objects					n03241335	drinking_fountain.n.01	misc	40
+1125	smoke detector	smoke detector	10	40	7		otherprop	Objects							misc	40
+13	pillows	pillow	9	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+1196	flip flops	flip flops	9	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1197	cloth	cloth	9	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1198	banner	banner	9	40	7		otherprop	Objects					n02788021	banner.n.01	misc	40
+1199	clothes hanger	clothes hanger	9	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	objects	39
+1200	whiteboard eraser	whiteboard eraser	9	40	7		otherprop	Objects							objects	39
+378	iron	iron	9	40	7		otherprop	Objects					n03584829	iron.n.04	objects	39
+591	instrument case	instrument case	9	40	7	case	otherprop	Objects							objects	39
+49	toilet paper rolls	toilet paper	9	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+92	soap	soap	9	40	7	soap	otherprop	Objects					n04253437	soap.n.01	objects	39
+1098	block	block	9	40	7		otherprop	Objects							misc	40
+291	wall hanging	wall hanging	8	40	7		otherprop	Objects					n03491178	hanging.n.01	picture	6
+1063	kitchen island	kitchen island	8	38	7	kitchen island	otherstructure	Objects					n03620600	kitchen_island.n.01	counter	26
+107	pipes	pipe	8	38	7		otherstructure	Objects							misc	40
+1135	toothbrush	toothbrush	8	40	7	toothbrush	otherprop	Objects					n04453156	toothbrush.n.01	objects	39
+189	shirt	shirt	8	40	7		otherprop	Objects					n04197391	shirt.n.01	clothes	38
+245	cutting board	cutting board	8	40	7	cutting board	otherprop	Objects					n03025513	chopping_board.n.01	objects	39
+194	vase	vase	8	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1201	shower control valve	shower control valve	8	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+386	exercise machine	exercise machine	8	40	7	machine	otherprop	Objects							gym_equipment	33
+1202	compost bin	compost bin	8	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+857	shorts	shorts	8	40	7	shorts	otherprop	Objects							clothes	38
+452	tire	tire	8	40	7		otherprop	Objects					n04440749	tire.n.01	objects	39
+1203	teddy bear	teddy bear	7	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+346	bathrobe	bathrobe	7	40	7		otherprop	Objects					n02807616	bathrobe.n.01	clothes	38
+152	handrail	handrail	7	38	7	railing	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+83	faucet	faucet	7	40	7	faucet	otherprop	Objects			faucet	3325088	n03325088	faucet.n.01	misc	40
+1204	pantry wall	pantry wall	7	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+726	thermos	thermos	7	40	7	flask	otherprop	Objects	bottle		bottle	2876657	n04422727	thermos.n.01	objects	39
+61	rug	rug	7	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+39	couch cushions	cushion	7	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1117	tripod	tripod	7	39	6	stand	otherfurniture	Furniture					n04485082	tripod.n.01	objects	39
+540	mailbox	mailbox	7	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+1205	tupperware	tupperware	7	40	7		otherprop	Objects							objects	39
+415	shoe rack	shoe rack	7	40	7	shoe rack	otherprop	Objects							shelving	31
+31	towels	towel	6	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+1206	beer bottles	beer bottle	6	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+153	treadmill	treadmill	6	39	6	treadmill	otherfurniture	Furniture					n04477387	treadmill.n.01	gym_equipment	33
+1207	salt	salt	6	40	7		otherprop	Objects							objects	39
+129	chest	chest	6	39	6	chest	otherfurniture	Furniture	dresser	dresser					chest_of_drawers	13
+220	dispenser	dispenser	6	40	7		otherprop	Objects					n03210683	dispenser.n.01	objects	39
+1208	mirror doors	mirror door	6	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+231	remote	remote	6	40	7		otherprop	Objects			remote_control	4074963	n04074963	remote_control.n.01	objects	39
+1209	folded ladder	folded ladder	6	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	misc	40
+39	cushion	cushion	6	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1210	carton	carton	6	40	7		otherprop	Objects							objects	39
+117	step	step	6	38	7		otherstructure	Objects					n04314914	step.n.04	misc	40
+822	drying rack	drying rack	6	39	6	drying rack	otherfurniture	Furniture							shelving	31
+238	slippers	slipper	6	40	7	shoe	otherprop	Objects					n04241394	slipper.n.01	clothes	38
+143	pool table	pool table	6	39	6	pool table	otherfurniture	Furniture	table	table	table	4379243	n03982430	pool_table.n.01	table	5
+1211	soda stream	soda stream	6	40	7		otherprop	Objects							objects	39
+228	toilet brush	toilet brush	6	40	7	toilet brush	otherprop	Objects							objects	39
+494	loft bed	loft bed	6	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+226	cooking pot	cooking pot	6	40	7	pot	otherprop	Objects							objects	39
+91	heater	heater	6	39	6	heater	otherfurniture	Furniture					n03508101	heater.n.01	misc	40
+1072	messenger bag	messenger bag	6	37	7	bag	bag	Objects							objects	39
+435	stapler	stapler	6	40	7	stapler	otherprop	Objects					n04303497	stapler.n.01	objects	39
+1165	closet walls	closet wall	5	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+345	scanner	scanner	5	40	7		otherprop	Objects							appliances	37
+893	elliptical machine	elliptical machine	5	40	7	machine	otherprop	Objects							gym_equipment	33
+621	kettle	kettle	5	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1212	metronome	metronome	5	40	7		otherprop	Objects					n03757604	metronome.n.01	objects	39
+297	dumbell	dumbell	5	40	7		otherprop	Objects							objects	39
+1213	music book	music book	5	23	2	book	books	Books					n02870526	book.n.11	objects	39
+1214	rice cooker	rice cooker	5	40	7		otherprop	Objects							objects	39
+1215	dart board	dart board	5	38	7	board	otherstructure	Objects					n03162940	dartboard.n.01	objects	39
+529	sewing machine	sewing machine	5	40	7	sewing machine	otherprop	Objects					n04179913	sewing_machine.n.01	objects	39
+1216	grab bar	grab bar	5	38	7	railing	otherstructure	Objects							railing	30
+1217	flowerpot	flowerpot	5	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1218	painting	painting	5	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1219	railing	railing	5	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+1220	stair	stair	5	38	7	stairs	otherstructure	Objects	stairs				n04314914	step.n.04	stairs	16
+525	toolbox	toolbox	5	39	6	chest	otherfurniture	Furniture					n04452615	toolbox.n.01	objects	39
+204	nerf gun	nerf gun	5	40	7		otherprop	Objects							objects	39
+693	binders	binder	5	40	7	binder	otherprop	Objects							objects	39
+179	desk lamp	desk lamp	5	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1221	quadcopter	quadcopter	5	40	7		otherprop	Objects							objects	39
+1222	pitcher	pitcher	5	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+1223	hanging	hanging	5	40	7		otherprop	Objects							misc	40
+1224	mail	mail	5	40	7		otherprop	Objects							misc	40
+1225	closet ceiling	closet ceiling	5	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+1226	hoverboard	hoverboard	5	40	7		otherprop	Objects							objects	39
+1227	beanbag chair	beanbag chair	5	39	6	bean bag	otherfurniture	Furniture					n02816656	beanbag.n.01	chair	3
+571	water heater	water heater	5	40	7	water heater	otherprop	Objects					n04560113	water_heater.n.01	misc	40
+1228	spray bottle	spray bottle	5	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+556	rope	rope	5	40	7	rope	otherprop	Objects					n04108268	rope.n.01	objects	39
+280	plastic container	plastic container	5	40	7	container	otherprop	Objects							objects	39
+1229	soap bottle	soap bottle	5	40	7	soap	otherprop	Objects							objects	39
+1230	ikea bag	ikea bag	4	37	7	bag	bag	Objects				2773838	n02773838	bag.n.06	objects	39
+1231	sleeping bag	sleeping bag	4	40	7		otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+1232	duffel bag	duffel bag	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+746	frying pan	frying pan	4	40	7	frying pan	otherprop	Objects					n03400231	frying_pan.n.01	objects	39
+1233	oven mitt	oven mitt	4	40	7		otherprop	Objects							objects	39
+1234	pot	pot	4	40	7	pot	otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+144	hand dryer	hand dryer	4	40	7		otherprop	Objects							objects	39
+282	dollhouse	dollhouse	4	39	6	doll house	otherfurniture	Furniture					n03219483	dollhouse.n.01	objects	39
+167	shampoo bottle	shampoo bottle	4	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1235	hair brush	hair brush	4	40	7		otherprop	Objects					n02908217	brush.n.02	objects	39
+1236	tennis racket	tennis racket	4	40	7		otherprop	Objects					n04409806	tennis_racket.n.01	objects	39
+1237	display case	display case	4	40	7	case	otherprop	Objects							objects	39
+234	ping pong table	ping pong table	4	39	6	ping pong table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+563	boiler	boiler	4	40	7		otherprop	Objects							misc	40
+1238	bag of coffee beans	bag of coffee beans	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+1239	bananas	banana	4	40	7		otherprop	Objects					n00021265	food.n.01	objects	39
+1240	carseat	carseat	4	40	7		otherprop	Objects							misc	40
+366	helmet	helmet	4	40	7		otherprop	Objects			helmet	3513137	n03513137	helmet.n.02	clothes	38
+816	umbrella	umbrella	4	40	7	umbrella	otherprop	Objects					n04507155	umbrella.n.01	objects	39
+1241	coffee box	coffee box	4	40	7		otherprop	Objects							objects	39
+719	envelope	envelope	4	40	7	envelope	otherprop	Objects					n03291819	envelope.n.01	objects	39
+284	wet floor sign	wet floor sign	4	40	7	sign	otherprop	Objects							misc	40
+1242	clothing rack	clothing rack	4	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+247	controller	controller	4	40	7		otherprop	Objects					n03096960	control.n.09	objects	39
+1243	bath walls	bathroom wall	4	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+1244	podium	podium	4	39	6		otherfurniture	Furniture					n03159640	dais.n.01	furniture	36
+1245	storage box	storage box	4	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1246	dolly	dolly	4	40	7		otherprop	Objects							misc	40
+1247	shampoo	shampoo	3	40	7		otherprop	Objects					n04183516	shampoo.n.01	objects	39
+592	paper tray	paper tray	3	40	7	paper tray	otherprop	Objects							objects	39
+385	cabinet door	cabinet door	3	8	12	door	door	Wall	door						door	4
+1248	changing station	changing station	3	40	7		otherprop	Objects							misc	40
+1249	poster printer	poster printer	3	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+133	screen	screen	3	40	7		otherprop	Objects					n03151077	curtain.n.01	curtain	12
+301	soap bar	soap bar	3	38	7	bar	otherstructure	Objects							objects	39
+1250	crutches	crutches	3	40	7		otherprop	Objects					n03141823	crutch.n.01	objects	39
+379	studio light	studio light	3	38	7	light	otherstructure	Objects							lighting	28
+130	stack of cups	cup	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+1251	toilet flush button	toilet flush button	3	40	7		otherprop	Objects							objects	39
+450	trunk	trunk	3	40	7		otherprop	Objects							misc	40
+1252	grocery bag	grocery bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03461288	grocery_bag.n.01	objects	39
+316	plastic bin	plastic bin	3	40	7	bin	otherprop	Objects							objects	39
+1253	pizza box	pizza box	3	29	7	box	box	Objects							objects	39
+385	cabinet doors	cabinet door	3	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	door	4
+1254	legs	legs	3	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+461	car	car	3	40	7	car	otherprop	Objects	car		car	2958343	n02958343	car.n.01	misc	40
+1255	shaving cream	shaving cream	3	40	7		otherprop	Objects					n04186051	shaving_cream.n.01	objects	39
+1256	luggage stand	luggage stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+599	shredder	shredder	3	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+281	statue	statue	3	40	7	sculpture	otherprop	Objects					n04306847	statue.n.01	misc	40
+1257	urinal	urinal	3	33	7	toilet	toilet	Objects	toilet	toilet			n04515991	urinal.n.01	toilet	18
+1258	hose	hose	3	40	7		otherprop	Objects					n03539875	hose.n.03	misc	40
+1259	bike pump	bike pump	3	40	7		otherprop	Objects							objects	39
+319	coatrack	coatrack	3	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1260	bear	bear	3	40	7		otherprop	Objects							objects	39
+28	wall lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1261	humidifier	humidifier	3	40	7		otherprop	Objects							objects	39
+546	toothpaste	toothpaste	3	40	7	toothpaste	otherprop	Objects							objects	39
+1262	mouthwash bottle	mouthwash bottle	3	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1263	poster cutter	poster cutter	3	40	7		otherprop	Objects							objects	39
+1264	golf bag	golf bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03445617	golf_bag.n.01	objects	39
+1265	food container	food container	3	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1266	camera	camera	3	40	7		otherprop	Objects							objects	39
+28	table lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n04380533	table_lamp.n.01	lighting	28
+1267	yoga mat	yoga mat	3	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1268	card	card	3	40	7		otherprop	Objects							objects	39
+1269	mug	mug	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+188	shower doors	shower door	3	38	7		otherstructure	Objects					n04208936	shower.n.01	door	4
+689	cardboard	cardboard	3	40	7		otherprop	Objects							objects	39
+1270	rack stand	rack stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+1271	boxes of paper	boxes of paper	3	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1272	flag	flag	3	40	7		otherprop	Objects							misc	40
+354	futon	futon	3	39	6	mattress	otherfurniture	Furniture					n03408444	futon.n.01	sofa	10
+339	magazine	magazine	3	40	7	magazine	otherprop	Objects					n06595351	magazine.n.01	objects	39
+1009	exit sign	exit sign	3	40	7	exit sign	otherprop	Objects							misc	40
+1273	rolled poster	rolled poster	3	40	7		otherprop	Objects							objects	39
+1274	wheel	wheel	3	40	7		otherprop	Objects							objects	39
+15	pictures	picture	3	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1275	blackboard eraser	blackboard eraser	3	40	7	eraser	otherprop	Objects					n03294833	eraser.n.01	objects	39
+361	organizer	organizer	3	40	7		otherprop	Objects					n03918737	personal_digital_assistant.n.01	objects	39
+1276	doll	doll	3	40	7	toy	otherprop	Objects					n03219135	doll.n.01	objects	39
+326	book rack	book rack	3	39	6	bookrack	otherfurniture	Furniture							objects	39
+1277	laundry bag	laundry bag	3	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+1278	sponge	sponge	3	40	7		otherprop	Objects					n01906749	sponge.n.04	objects	39
+116	seating	seat	3	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+1184	folded chairs	folded chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1279	lotion bottle	lotion bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+212	can	can	2	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1280	lunch box	lunch box	2	40	7		otherprop	Objects							objects	39
+1281	food display	food display	2	40	7		otherprop	Objects							misc	40
+794	storage shelf	storage shelf	2	40	7		otherprop	Objects							shelving	31
+1282	sliding wood door	sliding wood door	2	40	7		otherprop	Objects							door	4
+955	pants	pants	2	40	7		otherprop	Objects					n04489008	trouser.n.01	clothes	38
+387	wood	wood	2	40	7		otherprop	Objects							misc	40
+69	boards	board	2	38	7	board	otherstructure	Objects							board_panel	35
+65	bottles	bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+523	washcloth	washcloth	2	40	7		otherprop	Objects					n04554523	washcloth.n.01	towel	20
+389	workbench	workbench	2	39	6	bench	otherfurniture	Furniture	bench		table	4379243	n04600486	workbench.n.01	table	5
+29	open kitchen cabinet	kitchen cabinet	2	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+1283	organizer shelf	organizer shelf	2	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+146	frame	frame	2	38	7		otherstructure	Objects							misc	40
+130	cups	cup	2	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+372	exercise ball	exercise ball	2	40	7	ball	otherprop	Objects					n04285146	sports_equipment.n.01	gym_equipment	33
+289	easel	easel	2	39	6	stand	otherfurniture	Furniture					n03262809	easel.n.01	furniture	36
+440	garbage bag	garbage bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+321	roomba	roomba	2	40	7		otherprop	Objects							objects	39
+976	garage door	garage door	2	38	7	garage door	otherstructure	Objects	door						door	4
+1256	luggage rack	luggage stand	2	39	6	stand	otherfurniture	Furniture					n04038440		shelving	31
+1284	bike lock	bike lock	2	40	7		otherprop	Objects							objects	39
+1285	briefcase	briefcase	2	40	7		otherprop	Objects					n02900705	briefcase.n.01	objects	39
+357	hand towel	hand towel	2	27	7	towel	towel	Objects					n03490006	hand_towel.n.01	towel	20
+1286	bath products	bath product	2	40	7		otherprop	Objects							objects	39
+1287	star	star	2	40	7		otherprop	Objects					n09444783	star.n.03	misc	40
+365	map	map	2	40	7	map	otherprop	Objects					n03720163	map.n.01	misc	40
+1288	coffee bean bag	coffee bean bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+81	headboard	headboard	2	39	6	headboard	otherfurniture	Furniture					n03502200	headboard.n.01	bed	11
+1289	ipad	ipad	2	40	7		otherprop	Objects							objects	39
+1290	display rack	display rack	2	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+948	traffic cone	traffic cone	2	40	7	cone	otherprop	Objects	cone						objects	39
+174	toiletry	toiletry	2	40	7		otherprop	Objects					n04447443	toiletry.n.01	objects	39
+1028	canopy	canopy	2	40	7		otherprop	Objects							misc	40
+1291	massage chair	massage chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1292	paper organizer	paper organizer	2	40	7		otherprop	Objects							objects	39
+1005	barricade	barricade	2	40	7		otherprop	Objects							misc	40
+235	platform	platform	2	38	7		otherstructure	Objects							misc	40
+1293	cap	cap	2	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+1294	dumbbell plates	dumbbell plates	2	40	7		otherprop	Objects							objects	39
+1295	elevator	elevator	2	38	7		otherstructure	Objects							misc	40
+1296	cooking pan	cooking pan	2	40	7	pan	otherprop	Objects					n03880531	pan.n.01	objects	39
+1297	trash bag	trash bag	2	37	7	bag	bag	Objects							objects	39
+1298	santa	santa	2	40	7		otherprop	Objects							misc	40
+1299	jewelry box	jewelry box	2	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1300	boat	boat	2	40	7		otherprop	Objects							misc	40
+1301	sock	sock	2	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1051	kinect	kinect	2	40	7	kinect	otherprop	Objects							objects	39
+566	crib	crib	2	39	6	crib	otherfurniture	Furniture							furniture	36
+1302	plastic storage bin	plastic storage bin	2	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1062	cooler	cooler	2	24	6	refridgerator	refridgerator	Furniture					n03102654	cooler.n.01	appliances	37
+1303	kitchen apron	kitchen apron	2	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1304	dishwashing soap bottle	dishwashing soap bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1305	xbox controller	xbox controller	2	40	7		otherprop	Objects							objects	39
+1306	banana holder	banana holder	2	40	7		otherprop	Objects							objects	39
+298	ping pong paddle	ping pong paddle	2	40	7		otherprop	Objects							table	5
+1307	airplane	airplane	2	40	7		otherprop	Objects							misc	40
+1308	conditioner bottle	conditioner bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1309	tea kettle	tea kettle	2	40	7	tea kettle	otherprop	Objects					n04397768	teakettle.n.01	objects	39
+43	bedframe	bedframe	2	39	6		otherfurniture	Furniture					n02822579	bedstead.n.01	bed	11
+1310	wood beam	wood beam	2	38	7		otherstructure	Objects							beam	29
+593	toilet paper package	toilet paper package	2	40	7		otherprop	Objects							objects	39
+1311	wall mounted coat rack	wall mounted coat rack	2	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1312	film light	film light	2	40	7		otherprop	Objects							lighting	28
+749	ceiling lamp	ceiling lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+623	chain	chain	1	40	7		otherprop	Objects							chair	3
+1313	sofa	sofa	1	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+99	closet wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+265	sweater	sweater	1	40	7		otherprop	Objects					n04370048	sweater.n.01	clothes	38
+1314	kitchen mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+99	wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1315	water softener	water softener	1	40	7		otherprop	Objects							misc	40
+448	banister	banister	1	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+257	trolley	trolley	1	40	7	trolley	otherprop	Objects					n04335435	streetcar.n.01	misc	40
+1316	pantry shelf	pantry shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+786	sofa bed	sofa bed	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+801	loofa	loofa	1	40	7		otherprop	Objects							objects	39
+972	shower faucet handle	shower faucet handle	1	40	7	handle	otherprop	Objects							shower	23
+1317	toy piano	toy piano	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1318	fish	fish	1	40	7		otherprop	Objects					n02512053	fish.n.01	objects	39
+75	file cabinets	file cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n03337140	file.n.03	cabinet	7
+657	cat litter box	cat litter box	1	29	7	box	box	Objects							objects	39
+561	electric panel	electric panel	1	40	7		otherprop	Objects							misc	40
+93	suitcases	suitcase	1	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+513	curtain rod	curtain rod	1	38	7	curtain rod	otherstructure	Objects							curtain	12
+411	bunk bed	bunk bed	1	39	6	bunk bed	otherfurniture	Furniture	bed	bed	bed	2818832	n02920259	bunk_bed.n.01	bed	11
+1122	chandelier	chandelier	1	38	7	chandelier	otherstructure	Objects					n03005285	chandelier.n.01	lighting	28
+922	tape	tape	1	40	7	tape	otherprop	Objects							objects	39
+88	plates	plate	1	40	7		otherprop	Objects					n03959485	plate.n.04	objects	39
+518	alarm	alarm	1	40	7	alarm	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+814	fire hose	fire hose	1	40	7		otherprop	Objects					n03346004	fire_hose.n.01	misc	40
+1319	toy dinosaur	toy dinosaur	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1320	cone	cone	1	40	7		otherprop	Objects							objects	39
+649	glass doors	glass door	1	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+607	hatrack	hatrack	1	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+819	subwoofer	subwoofer	1	40	7	speaker	otherprop	Objects			speaker	3691459	n04349401	subwoofer.n.01	objects	39
+1321	fire sprinkler	fire sprinkler	1	40	7		otherprop	Objects							misc	40
+1322	trash cabinet	trash cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+1204	pantry walls	pantry wall	1	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+227	photo	photo	1	40	7	photo	otherprop	Objects					n03925226	photograph.n.01	picture	6
+817	barrier	barrier	1	40	7		otherprop	Objects					n02796623	barrier.n.01	misc	40
+130	stacks of cups	cup	1	40	7		otherprop	Objects					n03147509	cup.n.01	objects	39
+712	beachball	beachball	1	40	7	ball	otherprop	Objects					n02814224	beach_ball.n.01	objects	39
+1323	folded boxes	folded boxes	1	40	7		otherprop	Objects							objects	39
+1324	contact lens solution bottle	contact lens solution bottle	1	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+673	covered box	covered box	1	29	7	box	box	Objects							objects	39
+459	folder	folder	1	40	7	folder	otherprop	Objects					n03376279	folder.n.02	objects	39
+643	mail trays	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+238	slipper	slipper	1	40	7		otherprop	Objects					n04241394	slipper.n.01	clothes	38
+765	magazine rack	magazine rack	1	39	6	stand	otherfurniture	Furniture					n03704549	magazine_rack.n.01	shelving	31
+1008	sticker	sticker	1	40	7	sticker	otherprop	Objects					n07272545	gummed_label.n.01	objects	39
+225	lotion	lotion	1	40	7		otherprop	Objects					n03690938	lotion.n.01	objects	39
+1083	buddha	buddha	1	40	7		otherprop	Objects							objects	39
+813	file organizer	file organizer	1	40	7		otherprop	Objects							objects	39
+138	paper towel rolls	paper towel roll	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+1145	night lamp	night lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+796	fuse box	fuse box	1	40	7		otherprop	Objects							misc	40
+1325	knife block	knife block	1	40	7		otherprop	Objects							objects	39
+363	furnace	furnace	1	39	6	furnace	otherfurniture	Furniture					n03404449	furnace.n.01		
+1174	cd cases	cd case	1	40	7		otherprop	Objects							objects	39
+38	stools	stool	1	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+1326	hand sanitzer dispenser	hand sanitzer dispenser	1	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+997	teapot	teapot	1	40	7	tea pot	otherprop	Objects					n04398044	teapot.n.01	objects	39
+1327	pen holder	pen holder	1	40	7		otherprop	Objects							objects	39
+1328	tray rack	tray rack	1	40	7		otherprop	Objects							objects	39
+1329	wig	wig	1	40	7		otherprop	Objects					n04584207	wig.n.01	objects	39
+182	switch	switch	1	40	7		otherprop	Objects					n04372370	switch.n.01	misc	40
+280	plastic containers	plastic container	1	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1330	night light	night light	1	40	7		otherprop	Objects							lighting	28
+1331	notepad	notepad	1	40	7		otherprop	Objects							objects	39
+1332	mail bin	mail bin	1	40	7		otherprop	Objects							misc	40
+1333	elevator button	elevator button	1	40	7		otherprop	Objects							misc	40
+939	gaming wheel	gaming wheel	1	40	7		otherprop	Objects							objects	39
+1334	drum set	drum set	1	40	7		otherprop	Objects							objects	39
+480	cosmetic bag	cosmetic bag	1	37	7	bag	bag	Objects							objects	39
+907	coffee mug	coffee mug	1	40	7	vessel	otherprop	Objects			cup or mug	3797390	n03063599	coffee_mug.n.01	objects	39
+1335	closet shelf	closet shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
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scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt

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scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py

@@ -0,0 +1,255 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+import argparse
+import glob
+import json
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+# Load external constants
+from meta_data.scannet200_constants import VALID_CLASS_IDS_200, VALID_CLASS_IDS_20
+
+CLOUD_FILE_PFIX = "_vh_clean_2"
+SEGMENTS_FILE_PFIX = ".0.010000.segs.json"
+AGGREGATIONS_FILE_PFIX = ".aggregation.json"
+CLASS_IDS200 = VALID_CLASS_IDS_200
+CLASS_IDS20 = VALID_CLASS_IDS_20
+IGNORE_INDEX = -1
+
+
+def read_plymesh(filepath):
+    """Read ply file and return it as numpy array. Returns None if emtpy."""
+    with open(filepath, "rb") as f:
+        plydata = plyfile.PlyData.read(f)
+    if plydata.elements:
+        vertices = pd.DataFrame(plydata["vertex"].data).values
+        faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+        return vertices, faces
+
+
+# Map the raw category id to the point cloud
+def point_indices_from_group(seg_indices, group, labels_pd):
+    group_segments = np.array(group["segments"])
+    label = group["label"]
+
+    # Map the category name to id
+    label_id20 = labels_pd[labels_pd["raw_category"] == label]["nyu40id"]
+    label_id20 = int(label_id20.iloc[0]) if len(label_id20) > 0 else 0
+    label_id200 = labels_pd[labels_pd["raw_category"] == label]["id"]
+    label_id200 = int(label_id200.iloc[0]) if len(label_id200) > 0 else 0
+
+    # Only store for the valid categories
+    if label_id20 in CLASS_IDS20:
+        label_id20 = CLASS_IDS20.index(label_id20)
+    else:
+        label_id20 = IGNORE_INDEX
+
+    if label_id200 in CLASS_IDS200:
+        label_id200 = CLASS_IDS200.index(label_id200)
+    else:
+        label_id200 = IGNORE_INDEX
+
+    # get points, where segment indices (points labelled with segment ids) are in the group segment list
+    point_idx = np.where(np.isin(seg_indices, group_segments))[0]
+    return point_idx, label_id20, label_id200
+
+
+def face_normal(vertex, face):
+    v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+    v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+    vec = np.cross(v01, v02)
+    length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+    nf = vec / length
+    area = length * 0.5
+    return nf, area
+
+
+def vertex_normal(vertex, face):
+    nf, area = face_normal(vertex, face)
+    nf = nf * area
+
+    nv = np.zeros_like(vertex)
+    for i in range(face.shape[0]):
+        nv[face[i]] += nf[i]
+
+    length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+    nv = nv / length
+    return nv
+
+
+def handle_process(
+    scene_path, output_path, labels_pd, train_scenes, val_scenes, parse_normals=True
+):
+    scene_id = os.path.basename(scene_path)
+    mesh_path = os.path.join(scene_path, f"{scene_id}{CLOUD_FILE_PFIX}.ply")
+    segments_file = os.path.join(
+        scene_path, f"{scene_id}{CLOUD_FILE_PFIX}{SEGMENTS_FILE_PFIX}"
+    )
+    aggregations_file = os.path.join(scene_path, f"{scene_id}{AGGREGATIONS_FILE_PFIX}")
+    info_file = os.path.join(scene_path, f"{scene_id}.txt")
+
+    if scene_id in train_scenes:
+        output_path = os.path.join(output_path, "train", f"{scene_id}")
+        split_name = "train"
+    elif scene_id in val_scenes:
+        output_path = os.path.join(output_path, "val", f"{scene_id}")
+        split_name = "val"
+    else:
+        output_path = os.path.join(output_path, "test", f"{scene_id}")
+        split_name = "test"
+
+    print(f"Processing: {scene_id} in {split_name}")
+
+    vertices, faces = read_plymesh(mesh_path)
+    coords = vertices[:, :3]
+    colors = vertices[:, 3:6]
+    save_dict = dict(
+        coord=coords.astype(np.float32),
+        color=colors.astype(np.uint8),
+    )
+
+    # # Rotating the mesh to axis aligned
+    # info_dict = {}
+    # with open(info_file) as f:
+    #     for line in f:
+    #         (key, val) = line.split(" = ")
+    #         info_dict[key] = np.fromstring(val, sep=' ')
+    #
+    # if 'axisAlignment' not in info_dict:
+    #     rot_matrix = np.identity(4)
+    # else:
+    #     rot_matrix = info_dict['axisAlignment'].reshape(4, 4)
+    # r_coords = coords.transpose()
+    # r_coords = np.append(r_coords, np.ones((1, r_coords.shape[1])), axis=0)
+    # r_coords = np.dot(rot_matrix, r_coords)
+    # coords = r_coords
+
+    # Parse Normals
+    if parse_normals:
+        save_dict["normal"] = vertex_normal(coords, faces).astype(np.float32)
+
+    # Load segments file
+    if split_name != "test":
+        with open(segments_file) as f:
+            segments = json.load(f)
+            seg_indices = np.array(segments["segIndices"])
+
+        # Load Aggregations file
+        with open(aggregations_file) as f:
+            aggregation = json.load(f)
+            seg_groups = np.array(aggregation["segGroups"])
+
+        # Generate new labels
+        semantic_gt20 = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+        semantic_gt200 = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+        instance_ids = np.ones((vertices.shape[0]), dtype=np.int16) * IGNORE_INDEX
+        for group in seg_groups:
+            point_idx, label_id20, label_id200 = point_indices_from_group(
+                seg_indices, group, labels_pd
+            )
+
+            semantic_gt20[point_idx] = label_id20
+            semantic_gt200[point_idx] = label_id200
+            instance_ids[point_idx] = group["id"]
+
+        semantic_gt20 = semantic_gt20.astype(int)
+        semantic_gt200 = semantic_gt200.astype(int)
+        instance_ids = instance_ids.astype(int)
+
+        save_dict["segment20"] = semantic_gt20
+        save_dict["segment200"] = semantic_gt200
+        save_dict["instance"] = instance_ids
+
+        # Concatenate with original cloud
+        processed_vertices = np.hstack((semantic_gt200, instance_ids))
+
+        if np.any(np.isnan(processed_vertices)) or not np.all(
+            np.isfinite(processed_vertices)
+        ):
+            raise ValueError(f"Find NaN in Scene: {scene_id}")
+
+    # Save processed data
+    os.makedirs(output_path, exist_ok=True)
+    for key in save_dict.keys():
+        np.save(os.path.join(output_path, f"{key}.npy"), save_dict[key])
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    parser.add_argument(
+        "--parse_normals", default=True, type=bool, help="Whether parse point normals"
+    )
+    parser.add_argument(
+        "--num_workers",
+        default=mp.cpu_count(),
+        type=int,
+        help="Num workers for preprocessing.",
+    )
+    config = parser.parse_args()
+
+    # Load label map
+    labels_pd = pd.read_csv(
+        "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv",
+        sep="\t",
+        header=0,
+    )
+
+    # Load train/val splits
+    with open(
+        "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_train.txt"
+    ) as train_file:
+        train_scenes = train_file.read().splitlines()
+    with open(
+        "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt"
+    ) as val_file:
+        val_scenes = val_file.read().splitlines()
+
+    # Create output directories
+    train_output_dir = os.path.join(config.output_root, "train")
+    os.makedirs(train_output_dir, exist_ok=True)
+    val_output_dir = os.path.join(config.output_root, "val")
+    os.makedirs(val_output_dir, exist_ok=True)
+    test_output_dir = os.path.join(config.output_root, "test")
+    os.makedirs(test_output_dir, exist_ok=True)
+
+    # Load scene paths
+    scene_paths = sorted(glob.glob(config.dataset_root + "/scans*/scene*"))
+
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=config.num_workers)
+    _ = list(
+        pool.map(
+            handle_process,
+            scene_paths,
+            repeat(config.output_root),
+            repeat(labels_pd),
+            repeat(train_scenes),
+            repeat(val_scenes),
+            repeat(config.parse_normals),
+        )
+    )

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py

@@ -0,0 +1,183 @@
+import os, struct
+import numpy as np
+import zlib
+import imageio
+import cv2
+
+COMPRESSION_TYPE_COLOR = {-1: "unknown", 0: "raw", 1: "png", 2: "jpeg"}
+COMPRESSION_TYPE_DEPTH = {
+    -1: "unknown",
+    0: "raw_ushort",
+    1: "zlib_ushort",
+    2: "occi_ushort",
+}
+
+
+class RGBDFrame:
+    def load(self, file_handle):
+        self.camera_to_world = np.asarray(
+            struct.unpack("f" * 16, file_handle.read(16 * 4)), dtype=np.float32
+        ).reshape(4, 4)
+        self.timestamp_color = struct.unpack("Q", file_handle.read(8))[0]
+        self.timestamp_depth = struct.unpack("Q", file_handle.read(8))[0]
+        self.color_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+        self.depth_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+        self.color_data = b"".join(
+            struct.unpack(
+                "c" * self.color_size_bytes, file_handle.read(self.color_size_bytes)
+            )
+        )
+        self.depth_data = b"".join(
+            struct.unpack(
+                "c" * self.depth_size_bytes, file_handle.read(self.depth_size_bytes)
+            )
+        )
+
+    def decompress_depth(self, compression_type):
+        if compression_type == "zlib_ushort":
+            return self.decompress_depth_zlib()
+        else:
+            raise
+
+    def decompress_depth_zlib(self):
+        return zlib.decompress(self.depth_data)
+
+    def decompress_color(self, compression_type):
+        if compression_type == "jpeg":
+            return self.decompress_color_jpeg()
+        else:
+            raise
+
+    def decompress_color_jpeg(self):
+        return imageio.imread(self.color_data)
+
+
+class SensorData:
+    def __init__(self, filename):
+        self.version = 4
+        self.load(filename)
+
+    def load(self, filename):
+        with open(filename, "rb") as f:
+            version = struct.unpack("I", f.read(4))[0]
+            assert self.version == version
+            strlen = struct.unpack("Q", f.read(8))[0]
+            self.sensor_name = b"".join(struct.unpack("c" * strlen, f.read(strlen)))
+            self.intrinsic_color = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.extrinsic_color = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.intrinsic_depth = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.extrinsic_depth = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.color_compression_type = COMPRESSION_TYPE_COLOR[
+                struct.unpack("i", f.read(4))[0]
+            ]
+            self.depth_compression_type = COMPRESSION_TYPE_DEPTH[
+                struct.unpack("i", f.read(4))[0]
+            ]
+            self.color_width = struct.unpack("I", f.read(4))[0]
+            self.color_height = struct.unpack("I", f.read(4))[0]
+            self.depth_width = struct.unpack("I", f.read(4))[0]
+            self.depth_height = struct.unpack("I", f.read(4))[0]
+            self.depth_shift = struct.unpack("f", f.read(4))[0]
+            num_frames = struct.unpack("Q", f.read(8))[0]
+            self.frames = []
+            for i in range(num_frames):
+                frame = RGBDFrame()
+                frame.load(f)
+                self.frames.append(frame)
+
+    def export_depth_images(self, output_path, image_size=None, frame_skip=1):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print(
+            "exporting", len(self.frames) // frame_skip, " depth frames to", output_path
+        )
+        for f in range(0, len(self.frames), frame_skip):
+            if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+                continue
+            if f % 100 == 0:
+                print(
+                    "exporting",
+                    f,
+                    "th depth frames to",
+                    os.path.join(output_path, str(f) + ".png"),
+                )
+
+            depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
+            depth = np.fromstring(depth_data, dtype=np.uint16).reshape(
+                self.depth_height, self.depth_width
+            )
+            if image_size is not None:
+                depth = cv2.resize(
+                    depth,
+                    (image_size[1], image_size[0]),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+            imageio.imwrite(os.path.join(output_path, str(f) + ".png"), depth)
+
+    def export_color_images(self, output_path, image_size=None, frame_skip=1):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print(
+            "exporting", len(self.frames) // frame_skip, "color frames to", output_path
+        )
+        for f in range(0, len(self.frames), frame_skip):
+            if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+                continue
+            if f % 100 == 0:
+                print(
+                    "exporting",
+                    f,
+                    "th color frames to",
+                    os.path.join(output_path, str(f) + ".png"),
+                )
+            color = self.frames[f].decompress_color(self.color_compression_type)
+            if image_size is not None:
+                color = cv2.resize(
+                    color,
+                    (image_size[1], image_size[0]),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+            # imageio.imwrite(os.path.join(output_path, str(f) + '.jpg'), color)
+            imageio.imwrite(os.path.join(output_path, str(f) + ".png"), color)
+
+    def save_mat_to_file(self, matrix, filename):
+        with open(filename, "w") as f:
+            for line in matrix:
+                np.savetxt(f, line[np.newaxis], fmt="%f")
+
+    def export_poses(self, output_path, frame_skip=1):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print(
+            "exporting", len(self.frames) // frame_skip, "camera poses to", output_path
+        )
+        for f in range(0, len(self.frames), frame_skip):
+            self.save_mat_to_file(
+                self.frames[f].camera_to_world,
+                os.path.join(output_path, str(f) + ".txt"),
+            )
+
+    def export_intrinsics(self, output_path):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print("exporting camera intrinsics to", output_path)
+        self.save_mat_to_file(
+            self.intrinsic_color, os.path.join(output_path, "intrinsic_color.txt")
+        )
+        self.save_mat_to_file(
+            self.extrinsic_color, os.path.join(output_path, "extrinsic_color.txt")
+        )
+        self.save_mat_to_file(
+            self.intrinsic_depth, os.path.join(output_path, "intrinsic_depth.txt")
+        )
+        self.save_mat_to_file(
+            self.extrinsic_depth, os.path.join(output_path, "extrinsic_depth.txt")
+        )

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py

@@ -0,0 +1,91 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import torch
+import numpy as np
+import math
+import glob, os
+import argparse
+import open3d as o3d
+
+
+def make_open3d_point_cloud(xyz, color=None, voxel_size=None):
+    if np.isnan(xyz).any():
+        return None
+
+    xyz = xyz[:, :3]
+    pcd = o3d.geometry.PointCloud()
+    pcd.points = o3d.utility.Vector3dVector(xyz)
+    if color is not None:
+        pcd.colors = o3d.utility.Vector3dVector(color)
+    if voxel_size is not None:
+        pcd = pcd.voxel_down_sample(voxel_size)
+
+    return pcd
+
+
+def compute_overlap_ratio(pcd0, pcd1, voxel_size):
+    pcd0_down = pcd0.voxel_down_sample(voxel_size)
+    pcd1_down = pcd1.voxel_down_sample(voxel_size)
+    matching01 = get_matching_indices(pcd0_down, pcd1_down, voxel_size * 1.5, 1)
+    matching10 = get_matching_indices(pcd1_down, pcd0_down, voxel_size * 1.5, 1)
+    overlap0 = float(len(matching01)) / float(len(pcd0_down.points))
+    overlap1 = float(len(matching10)) / float(len(pcd1_down.points))
+    return max(overlap0, overlap1)
+
+
+def get_matching_indices(source, pcd_tree, search_voxel_size, K=None):
+    match_inds = []
+    for i, point in enumerate(source.points):
+        [_, idx, _] = pcd_tree.search_radius_vector_3d(point, search_voxel_size)
+        if K is not None:
+            idx = idx[:K]
+        for j in idx:
+            match_inds.append((i, j))
+    return match_inds
+
+
+def compute_full_overlapping(data_root, scene_id, voxel_size=0.05):
+    _points = [
+        (
+            pcd_name,
+            make_open3d_point_cloud(
+                torch.load(pcd_name)["coord"], voxel_size=voxel_size
+            ),
+        )
+        for pcd_name in glob.glob(os.path.join(data_root, scene_id, "pcd", "*.pth"))
+    ]
+    points = [(pcd_name, pcd) for (pcd_name, pcd) in _points if pcd is not None]
+    print(
+        "load {} point clouds ({} invalid has been filtered), computing matching/overlapping".format(
+            len(points), len(_points) - len(points)
+        )
+    )
+
+    matching_matrix = np.zeros((len(points), len(points)))
+    for i, (pcd0_name, pcd0) in enumerate(points):
+        print("matching to...{}".format(pcd0_name))
+        pcd0_tree = o3d.geometry.KDTreeFlann(copy.deepcopy(pcd0))
+        for j, (pcd1_name, pcd1) in enumerate(points):
+            if i == j:
+                continue
+            matching_matrix[i, j] = float(
+                len(get_matching_indices(pcd1, pcd0_tree, 1.5 * voxel_size, 1))
+            ) / float(len(pcd1.points))
+
+    # write to file
+    with open(os.path.join(data_root, scene_id, "pcd", "overlap.txt"), "w") as f:
+        for i, (pcd0_name, pcd0) in enumerate(points):
+            for j, (pcd1_name, pcd1) in enumerate(points):
+                if i < j:
+                    overlap = max(matching_matrix[i, j], matching_matrix[j, i])
+                    f.write(
+                        "{} {} {}\n".format(
+                            pcd0_name.replace(data_root, ""),
+                            pcd1_name.replace(data_root, ""),
+                            overlap,
+                        )
+                    )

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py

@@ -0,0 +1,33 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import argparse
+import glob, os, sys
+
+from SensorData import SensorData
+
+# params
+parser = argparse.ArgumentParser()
+# data paths
+parser.add_argument("--target_dir", required=True, help="path to the target dir")
+
+opt = parser.parse_args()
+print(opt)
+
+
+def main():
+    overlaps = glob.glob(os.path.join(opt.target_dir, "*/pcd/overlap.txt"))
+    with open(os.path.join(opt.target_dir, "overlap30.txt"), "w") as f:
+        for fo in overlaps:
+            for line in open(fo):
+                pcd0, pcd1, op = line.strip().split()
+                if float(op) >= 0.3:
+                    print("{} {} {}".format(pcd0, pcd1, op), file=f)
+    print("done")
+
+
+if __name__ == "__main__":
+    main()

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/plyfile.py

@@ -0,0 +1,894 @@
+#   Copyright 2014 Darsh Ranjan
+#
+#   This file is part of python-plyfile.
+#
+#   python-plyfile is free software: you can redistribute it and/or
+#   modify it under the terms of the GNU General Public License as
+#   published by the Free Software Foundation, either version 3 of the
+#   License, or (at your option) any later version.
+#
+#   python-plyfile is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+#   General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License
+#   along with python-plyfile.  If not, see
+#       <http://www.gnu.org/licenses/>.
+
+from itertools import islice as _islice
+
+import numpy as _np
+from sys import byteorder as _byteorder
+
+
+try:
+    _range = xrange
+except NameError:
+    _range = range
+
+
+# Many-many relation
+_data_type_relation = [
+    ("int8", "i1"),
+    ("char", "i1"),
+    ("uint8", "u1"),
+    ("uchar", "b1"),
+    ("uchar", "u1"),
+    ("int16", "i2"),
+    ("short", "i2"),
+    ("uint16", "u2"),
+    ("ushort", "u2"),
+    ("int32", "i4"),
+    ("int", "i4"),
+    ("uint32", "u4"),
+    ("uint", "u4"),
+    ("float32", "f4"),
+    ("float", "f4"),
+    ("float64", "f8"),
+    ("double", "f8"),
+]
+
+_data_types = dict(_data_type_relation)
+_data_type_reverse = dict((b, a) for (a, b) in _data_type_relation)
+
+_types_list = []
+_types_set = set()
+for _a, _b in _data_type_relation:
+    if _a not in _types_set:
+        _types_list.append(_a)
+        _types_set.add(_a)
+    if _b not in _types_set:
+        _types_list.append(_b)
+        _types_set.add(_b)
+
+
+_byte_order_map = {"ascii": "=", "binary_little_endian": "<", "binary_big_endian": ">"}
+
+_byte_order_reverse = {"<": "binary_little_endian", ">": "binary_big_endian"}
+
+_native_byte_order = {"little": "<", "big": ">"}[_byteorder]
+
+
+def _lookup_type(type_str):
+    if type_str not in _data_type_reverse:
+        try:
+            type_str = _data_types[type_str]
+        except KeyError:
+            raise ValueError("field type %r not in %r" % (type_str, _types_list))
+
+    return _data_type_reverse[type_str]
+
+
+def _split_line(line, n):
+    fields = line.split(None, n)
+    if len(fields) == n:
+        fields.append("")
+
+    assert len(fields) == n + 1
+
+    return fields
+
+
+def make2d(array, cols=None, dtype=None):
+    """
+    Make a 2D array from an array of arrays.  The `cols' and `dtype'
+    arguments can be omitted if the array is not empty.
+
+    """
+    if (cols is None or dtype is None) and not len(array):
+        raise RuntimeError("cols and dtype must be specified for empty " "array")
+
+    if cols is None:
+        cols = len(array[0])
+
+    if dtype is None:
+        dtype = array[0].dtype
+
+    return _np.fromiter(array, [("_", dtype, (cols,))], count=len(array))["_"]
+
+
+class PlyParseError(Exception):
+    """
+    Raised when a PLY file cannot be parsed.
+
+    The attributes `element', `row', `property', and `message' give
+    additional information.
+
+    """
+
+    def __init__(self, message, element=None, row=None, prop=None):
+        self.message = message
+        self.element = element
+        self.row = row
+        self.prop = prop
+
+        s = ""
+        if self.element:
+            s += "element %r: " % self.element.name
+        if self.row is not None:
+            s += "row %d: " % self.row
+        if self.prop:
+            s += "property %r: " % self.prop.name
+        s += self.message
+
+        Exception.__init__(self, s)
+
+    def __repr__(self):
+        return (
+            "PlyParseError(%r, element=%r, row=%r, prop=%r)" % self.message,
+            self.element,
+            self.row,
+            self.prop,
+        )
+
+
+class PlyData(object):
+    """
+    PLY file header and data.
+
+    A PlyData instance is created in one of two ways: by the static
+    method PlyData.read (to read a PLY file), or directly from __init__
+    given a sequence of elements (which can then be written to a PLY
+    file).
+
+    """
+
+    def __init__(
+        self, elements=[], text=False, byte_order="=", comments=[], obj_info=[]
+    ):
+        """
+        elements: sequence of PlyElement instances.
+
+        text: whether the resulting PLY file will be text (True) or
+            binary (False).
+
+        byte_order: '<' for little-endian, '>' for big-endian, or '='
+            for native.  This is only relevant if `text' is False.
+
+        comments: sequence of strings that will be placed in the header
+            between the 'ply' and 'format ...' lines.
+
+        obj_info: like comments, but will be placed in the header with
+            "obj_info ..." instead of "comment ...".
+
+        """
+        if byte_order == "=" and not text:
+            byte_order = _native_byte_order
+
+        self.byte_order = byte_order
+        self.text = text
+
+        self.comments = list(comments)
+        self.obj_info = list(obj_info)
+        self.elements = elements
+
+    def _get_elements(self):
+        return self._elements
+
+    def _set_elements(self, elements):
+        self._elements = tuple(elements)
+        self._index()
+
+    elements = property(_get_elements, _set_elements)
+
+    def _get_byte_order(self):
+        return self._byte_order
+
+    def _set_byte_order(self, byte_order):
+        if byte_order not in ["<", ">", "="]:
+            raise ValueError("byte order must be '<', '>', or '='")
+
+        self._byte_order = byte_order
+
+    byte_order = property(_get_byte_order, _set_byte_order)
+
+    def _index(self):
+        self._element_lookup = dict((elt.name, elt) for elt in self._elements)
+        if len(self._element_lookup) != len(self._elements):
+            raise ValueError("two elements with same name")
+
+    @staticmethod
+    def _parse_header(stream):
+        """
+        Parse a PLY header from a readable file-like stream.
+
+        """
+        lines = []
+        comments = {"comment": [], "obj_info": []}
+        while True:
+            line = stream.readline().decode("ascii").strip()
+            fields = _split_line(line, 1)
+
+            if fields[0] == "end_header":
+                break
+
+            elif fields[0] in comments.keys():
+                lines.append(fields)
+            else:
+                lines.append(line.split())
+
+        a = 0
+        if lines[a] != ["ply"]:
+            raise PlyParseError("expected 'ply'")
+
+        a += 1
+        while lines[a][0] in comments.keys():
+            comments[lines[a][0]].append(lines[a][1])
+            a += 1
+
+        if lines[a][0] != "format":
+            raise PlyParseError("expected 'format'")
+
+        if lines[a][2] != "1.0":
+            raise PlyParseError("expected version '1.0'")
+
+        if len(lines[a]) != 3:
+            raise PlyParseError("too many fields after 'format'")
+
+        fmt = lines[a][1]
+
+        if fmt not in _byte_order_map:
+            raise PlyParseError("don't understand format %r" % fmt)
+
+        byte_order = _byte_order_map[fmt]
+        text = fmt == "ascii"
+
+        a += 1
+        while a < len(lines) and lines[a][0] in comments.keys():
+            comments[lines[a][0]].append(lines[a][1])
+            a += 1
+
+        return PlyData(
+            PlyElement._parse_multi(lines[a:]),
+            text,
+            byte_order,
+            comments["comment"],
+            comments["obj_info"],
+        )
+
+    @staticmethod
+    def read(stream):
+        """
+        Read PLY data from a readable file-like object or filename.
+
+        """
+        (must_close, stream) = _open_stream(stream, "read")
+        try:
+            data = PlyData._parse_header(stream)
+            for elt in data:
+                elt._read(stream, data.text, data.byte_order)
+        finally:
+            if must_close:
+                stream.close()
+
+        return data
+
+    def write(self, stream):
+        """
+        Write PLY data to a writeable file-like object or filename.
+
+        """
+        (must_close, stream) = _open_stream(stream, "write")
+        try:
+            stream.write(self.header.encode("ascii"))
+            stream.write(b"\r\n")
+            for elt in self:
+                elt._write(stream, self.text, self.byte_order)
+        finally:
+            if must_close:
+                stream.close()
+
+    @property
+    def header(self):
+        """
+        Provide PLY-formatted metadata for the instance.
+
+        """
+        lines = ["ply"]
+
+        if self.text:
+            lines.append("format ascii 1.0")
+        else:
+            lines.append("format " + _byte_order_reverse[self.byte_order] + " 1.0")
+
+        # Some information is lost here, since all comments are placed
+        # between the 'format' line and the first element.
+        for c in self.comments:
+            lines.append("comment " + c)
+
+        for c in self.obj_info:
+            lines.append("obj_info " + c)
+
+        lines.extend(elt.header for elt in self.elements)
+        lines.append("end_header")
+        return "\r\n".join(lines)
+
+    def __iter__(self):
+        return iter(self.elements)
+
+    def __len__(self):
+        return len(self.elements)
+
+    def __contains__(self, name):
+        return name in self._element_lookup
+
+    def __getitem__(self, name):
+        return self._element_lookup[name]
+
+    def __str__(self):
+        return self.header
+
+    def __repr__(self):
+        return "PlyData(%r, text=%r, byte_order=%r, " "comments=%r, obj_info=%r)" % (
+            self.elements,
+            self.text,
+            self.byte_order,
+            self.comments,
+            self.obj_info,
+        )
+
+
+def _open_stream(stream, read_or_write):
+    if hasattr(stream, read_or_write):
+        return (False, stream)
+    try:
+        return (True, open(stream, read_or_write[0] + "b"))
+    except TypeError:
+        raise RuntimeError("expected open file or filename")
+
+
+class PlyElement(object):
+    """
+    PLY file element.
+
+    A client of this library doesn't normally need to instantiate this
+    directly, so the following is only for the sake of documenting the
+    internals.
+
+    Creating a PlyElement instance is generally done in one of two ways:
+    as a byproduct of PlyData.read (when reading a PLY file) and by
+    PlyElement.describe (before writing a PLY file).
+
+    """
+
+    def __init__(self, name, properties, count, comments=[]):
+        """
+        This is not part of the public interface.  The preferred methods
+        of obtaining PlyElement instances are PlyData.read (to read from
+        a file) and PlyElement.describe (to construct from a numpy
+        array).
+
+        """
+        self._name = str(name)
+        self._check_name()
+        self._count = count
+
+        self._properties = tuple(properties)
+        self._index()
+
+        self.comments = list(comments)
+
+        self._have_list = any(isinstance(p, PlyListProperty) for p in self.properties)
+
+    @property
+    def count(self):
+        return self._count
+
+    def _get_data(self):
+        return self._data
+
+    def _set_data(self, data):
+        self._data = data
+        self._count = len(data)
+        self._check_sanity()
+
+    data = property(_get_data, _set_data)
+
+    def _check_sanity(self):
+        for prop in self.properties:
+            if prop.name not in self._data.dtype.fields:
+                raise ValueError("dangling property %r" % prop.name)
+
+    def _get_properties(self):
+        return self._properties
+
+    def _set_properties(self, properties):
+        self._properties = tuple(properties)
+        self._check_sanity()
+        self._index()
+
+    properties = property(_get_properties, _set_properties)
+
+    def _index(self):
+        self._property_lookup = dict((prop.name, prop) for prop in self._properties)
+        if len(self._property_lookup) != len(self._properties):
+            raise ValueError("two properties with same name")
+
+    def ply_property(self, name):
+        return self._property_lookup[name]
+
+    @property
+    def name(self):
+        return self._name
+
+    def _check_name(self):
+        if any(c.isspace() for c in self._name):
+            msg = "element name %r contains spaces" % self._name
+            raise ValueError(msg)
+
+    def dtype(self, byte_order="="):
+        """
+        Return the numpy dtype of the in-memory representation of the
+        data.  (If there are no list properties, and the PLY format is
+        binary, then this also accurately describes the on-disk
+        representation of the element.)
+
+        """
+        return [(prop.name, prop.dtype(byte_order)) for prop in self.properties]
+
+    @staticmethod
+    def _parse_multi(header_lines):
+        """
+        Parse a list of PLY element definitions.
+
+        """
+        elements = []
+        while header_lines:
+            (elt, header_lines) = PlyElement._parse_one(header_lines)
+            elements.append(elt)
+
+        return elements
+
+    @staticmethod
+    def _parse_one(lines):
+        """
+        Consume one element definition.  The unconsumed input is
+        returned along with a PlyElement instance.
+
+        """
+        a = 0
+        line = lines[a]
+
+        if line[0] != "element":
+            raise PlyParseError("expected 'element'")
+        if len(line) > 3:
+            raise PlyParseError("too many fields after 'element'")
+        if len(line) < 3:
+            raise PlyParseError("too few fields after 'element'")
+
+        (name, count) = (line[1], int(line[2]))
+
+        comments = []
+        properties = []
+        while True:
+            a += 1
+            if a >= len(lines):
+                break
+
+            if lines[a][0] == "comment":
+                comments.append(lines[a][1])
+            elif lines[a][0] == "property":
+                properties.append(PlyProperty._parse_one(lines[a]))
+            else:
+                break
+
+        return (PlyElement(name, properties, count, comments), lines[a:])
+
+    @staticmethod
+    def describe(data, name, len_types={}, val_types={}, comments=[]):
+        """
+        Construct a PlyElement from an array's metadata.
+
+        len_types and val_types can be given as mappings from list
+        property names to type strings (like 'u1', 'f4', etc., or
+        'int8', 'float32', etc.). These can be used to define the length
+        and value types of list properties.  List property lengths
+        always default to type 'u1' (8-bit unsigned integer), and value
+        types default to 'i4' (32-bit integer).
+
+        """
+        if not isinstance(data, _np.ndarray):
+            raise TypeError("only numpy arrays are supported")
+
+        if len(data.shape) != 1:
+            raise ValueError("only one-dimensional arrays are " "supported")
+
+        count = len(data)
+
+        properties = []
+        descr = data.dtype.descr
+
+        for t in descr:
+            if not isinstance(t[1], str):
+                raise ValueError("nested records not supported")
+
+            if not t[0]:
+                raise ValueError("field with empty name")
+
+            if len(t) != 2 or t[1][1] == "O":
+                # non-scalar field, which corresponds to a list
+                # property in PLY.
+
+                if t[1][1] == "O":
+                    if len(t) != 2:
+                        raise ValueError("non-scalar object fields not " "supported")
+
+                len_str = _data_type_reverse[len_types.get(t[0], "u1")]
+                if t[1][1] == "O":
+                    val_type = val_types.get(t[0], "i4")
+                    val_str = _lookup_type(val_type)
+                else:
+                    val_str = _lookup_type(t[1][1:])
+
+                prop = PlyListProperty(t[0], len_str, val_str)
+            else:
+                val_str = _lookup_type(t[1][1:])
+                prop = PlyProperty(t[0], val_str)
+
+            properties.append(prop)
+
+        elt = PlyElement(name, properties, count, comments)
+        elt.data = data
+
+        return elt
+
+    def _read(self, stream, text, byte_order):
+        """
+        Read the actual data from a PLY file.
+
+        """
+        if text:
+            self._read_txt(stream)
+        else:
+            if self._have_list:
+                # There are list properties, so a simple load is
+                # impossible.
+                self._read_bin(stream, byte_order)
+            else:
+                # There are no list properties, so loading the data is
+                # much more straightforward.
+                self._data = _np.fromfile(stream, self.dtype(byte_order), self.count)
+
+        if len(self._data) < self.count:
+            k = len(self._data)
+            del self._data
+            raise PlyParseError("early end-of-file", self, k)
+
+        self._check_sanity()
+
+    def _write(self, stream, text, byte_order):
+        """
+        Write the data to a PLY file.
+
+        """
+        if text:
+            self._write_txt(stream)
+        else:
+            if self._have_list:
+                # There are list properties, so serialization is
+                # slightly complicated.
+                self._write_bin(stream, byte_order)
+            else:
+                # no list properties, so serialization is
+                # straightforward.
+                self.data.astype(self.dtype(byte_order), copy=False).tofile(stream)
+
+    def _read_txt(self, stream):
+        """
+        Load a PLY element from an ASCII-format PLY file.  The element
+        may contain list properties.
+
+        """
+        self._data = _np.empty(self.count, dtype=self.dtype())
+
+        k = 0
+        for line in _islice(iter(stream.readline, b""), self.count):
+            fields = iter(line.strip().split())
+            for prop in self.properties:
+                try:
+                    self._data[prop.name][k] = prop._from_fields(fields)
+                except StopIteration:
+                    raise PlyParseError("early end-of-line", self, k, prop)
+                except ValueError:
+                    raise PlyParseError("malformed input", self, k, prop)
+            try:
+                next(fields)
+            except StopIteration:
+                pass
+            else:
+                raise PlyParseError("expected end-of-line", self, k)
+            k += 1
+
+        if k < self.count:
+            del self._data
+            raise PlyParseError("early end-of-file", self, k)
+
+    def _write_txt(self, stream):
+        """
+        Save a PLY element to an ASCII-format PLY file.  The element may
+        contain list properties.
+
+        """
+        for rec in self.data:
+            fields = []
+            for prop in self.properties:
+                fields.extend(prop._to_fields(rec[prop.name]))
+
+            _np.savetxt(stream, [fields], "%.18g", newline="\r\n")
+
+    def _read_bin(self, stream, byte_order):
+        """
+        Load a PLY element from a binary PLY file.  The element may
+        contain list properties.
+
+        """
+        self._data = _np.empty(self.count, dtype=self.dtype(byte_order))
+
+        for k in _range(self.count):
+            for prop in self.properties:
+                try:
+                    self._data[prop.name][k] = prop._read_bin(stream, byte_order)
+                except StopIteration:
+                    raise PlyParseError("early end-of-file", self, k, prop)
+
+    def _write_bin(self, stream, byte_order):
+        """
+        Save a PLY element to a binary PLY file.  The element may
+        contain list properties.
+
+        """
+        for rec in self.data:
+            for prop in self.properties:
+                prop._write_bin(rec[prop.name], stream, byte_order)
+
+    @property
+    def header(self):
+        """
+        Format this element's metadata as it would appear in a PLY
+        header.
+
+        """
+        lines = ["element %s %d" % (self.name, self.count)]
+
+        # Some information is lost here, since all comments are placed
+        # between the 'element' line and the first property definition.
+        for c in self.comments:
+            lines.append("comment " + c)
+
+        lines.extend(list(map(str, self.properties)))
+
+        return "\r\n".join(lines)
+
+    def __getitem__(self, key):
+        return self.data[key]
+
+    def __setitem__(self, key, value):
+        self.data[key] = value
+
+    def __str__(self):
+        return self.header
+
+    def __repr__(self):
+        return "PlyElement(%r, %r, count=%d, comments=%r)" % (
+            self.name,
+            self.properties,
+            self.count,
+            self.comments,
+        )
+
+
+class PlyProperty(object):
+    """
+    PLY property description.  This class is pure metadata; the data
+    itself is contained in PlyElement instances.
+
+    """
+
+    def __init__(self, name, val_dtype):
+        self._name = str(name)
+        self._check_name()
+        self.val_dtype = val_dtype
+
+    def _get_val_dtype(self):
+        return self._val_dtype
+
+    def _set_val_dtype(self, val_dtype):
+        self._val_dtype = _data_types[_lookup_type(val_dtype)]
+
+    val_dtype = property(_get_val_dtype, _set_val_dtype)
+
+    @property
+    def name(self):
+        return self._name
+
+    def _check_name(self):
+        if any(c.isspace() for c in self._name):
+            msg = "Error: property name %r contains spaces" % self._name
+            raise RuntimeError(msg)
+
+    @staticmethod
+    def _parse_one(line):
+        assert line[0] == "property"
+
+        if line[1] == "list":
+            if len(line) > 5:
+                raise PlyParseError("too many fields after " "'property list'")
+            if len(line) < 5:
+                raise PlyParseError("too few fields after " "'property list'")
+
+            return PlyListProperty(line[4], line[2], line[3])
+
+        else:
+            if len(line) > 3:
+                raise PlyParseError("too many fields after " "'property'")
+            if len(line) < 3:
+                raise PlyParseError("too few fields after " "'property'")
+
+            return PlyProperty(line[2], line[1])
+
+    def dtype(self, byte_order="="):
+        """
+        Return the numpy dtype description for this property (as a tuple
+        of strings).
+
+        """
+        return byte_order + self.val_dtype
+
+    def _from_fields(self, fields):
+        """
+        Parse from generator.  Raise StopIteration if the property could
+        not be read.
+
+        """
+        return _np.dtype(self.dtype()).type(next(fields))
+
+    def _to_fields(self, data):
+        """
+        Return generator over one item.
+
+        """
+        yield _np.dtype(self.dtype()).type(data)
+
+    def _read_bin(self, stream, byte_order):
+        """
+        Read data from a binary stream.  Raise StopIteration if the
+        property could not be read.
+
+        """
+        try:
+            return _np.fromfile(stream, self.dtype(byte_order), 1)[0]
+        except IndexError:
+            raise StopIteration
+
+    def _write_bin(self, data, stream, byte_order):
+        """
+        Write data to a binary stream.
+
+        """
+        _np.dtype(self.dtype(byte_order)).type(data).tofile(stream)
+
+    def __str__(self):
+        val_str = _data_type_reverse[self.val_dtype]
+        return "property %s %s" % (val_str, self.name)
+
+    def __repr__(self):
+        return "PlyProperty(%r, %r)" % (self.name, _lookup_type(self.val_dtype))
+
+
+class PlyListProperty(PlyProperty):
+    """
+    PLY list property description.
+
+    """
+
+    def __init__(self, name, len_dtype, val_dtype):
+        PlyProperty.__init__(self, name, val_dtype)
+
+        self.len_dtype = len_dtype
+
+    def _get_len_dtype(self):
+        return self._len_dtype
+
+    def _set_len_dtype(self, len_dtype):
+        self._len_dtype = _data_types[_lookup_type(len_dtype)]
+
+    len_dtype = property(_get_len_dtype, _set_len_dtype)
+
+    def dtype(self, byte_order="="):
+        """
+        List properties always have a numpy dtype of "object".
+
+        """
+        return "|O"
+
+    def list_dtype(self, byte_order="="):
+        """
+        Return the pair (len_dtype, val_dtype) (both numpy-friendly
+        strings).
+
+        """
+        return (byte_order + self.len_dtype, byte_order + self.val_dtype)
+
+    def _from_fields(self, fields):
+        (len_t, val_t) = self.list_dtype()
+
+        n = int(_np.dtype(len_t).type(next(fields)))
+
+        data = _np.loadtxt(list(_islice(fields, n)), val_t, ndmin=1)
+        if len(data) < n:
+            raise StopIteration
+
+        return data
+
+    def _to_fields(self, data):
+        """
+        Return generator over the (numerical) PLY representation of the
+        list data (length followed by actual data).
+
+        """
+        (len_t, val_t) = self.list_dtype()
+
+        data = _np.asarray(data, dtype=val_t).ravel()
+
+        yield _np.dtype(len_t).type(data.size)
+        for x in data:
+            yield x
+
+    def _read_bin(self, stream, byte_order):
+        (len_t, val_t) = self.list_dtype(byte_order)
+
+        try:
+            n = _np.fromfile(stream, len_t, 1)[0]
+        except IndexError:
+            raise StopIteration
+
+        data = _np.fromfile(stream, val_t, n)
+        if len(data) < n:
+            raise StopIteration
+
+        return data
+
+    def _write_bin(self, data, stream, byte_order):
+        """
+        Write data to a binary stream.
+
+        """
+        (len_t, val_t) = self.list_dtype(byte_order)
+
+        data = _np.asarray(data, dtype=val_t).ravel()
+
+        _np.array(data.size, dtype=len_t).tofile(stream)
+        data.tofile(stream)
+
+    def __str__(self):
+        len_str = _data_type_reverse[self.len_dtype]
+        val_str = _data_type_reverse[self.val_dtype]
+        return "property list %s %s %s" % (len_str, val_str, self.name)
+
+    def __repr__(self):
+        return "PlyListProperty(%r, %r, %r)" % (
+            self.name,
+            _lookup_type(self.len_dtype),
+            _lookup_type(self.val_dtype),
+        )

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/point_cloud_extractor.py

@@ -0,0 +1,98 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import glob, os
+import numpy as np
+import cv2
+import torch
+
+
+def extractor(input_path, output_path):
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+
+    # Load Depth Camera Intrinsic
+    depth_intrinsic = np.loadtxt(input_path + "/intrinsic/intrinsic_depth.txt")
+    print("Depth intrinsic: ")
+    print(depth_intrinsic)
+
+    # Compute Camrea Distance (just for demo, so you can choose the camera distance in frame sampling)
+    poses = sorted(
+        glob.glob(input_path + "/pose/*.txt"),
+        key=lambda a: int(os.path.basename(a).split(".")[0]),
+    )
+    depths = sorted(
+        glob.glob(input_path + "/depth/*.png"),
+        key=lambda a: int(os.path.basename(a).split(".")[0]),
+    )
+    colors = sorted(
+        glob.glob(input_path + "/color/*.png"),
+        key=lambda a: int(os.path.basename(a).split(".")[0]),
+    )
+
+    # # Get Aligned Point Clouds.
+    for ind, (pose, depth, color) in enumerate(zip(poses, depths, colors)):
+        name = os.path.basename(pose).split(".")[0]
+
+        if os.path.exists(output_path + "/{}.npz".format(name)):
+            continue
+
+        try:
+            print("=" * 50, ": {}".format(pose))
+            depth_img = cv2.imread(depth, -1)  # read 16bit grayscale image
+            mask = depth_img != 0
+            color_image = cv2.imread(color)
+            color_image = cv2.resize(color_image, (640, 480))
+            color_image = np.reshape(color_image[mask], [-1, 3])
+            colors = np.zeros_like(color_image)
+            colors[:, 0] = color_image[:, 2]
+            colors[:, 1] = color_image[:, 1]
+            colors[:, 2] = color_image[:, 0]
+
+            pose = np.loadtxt(poses[ind])
+            print("Camera pose: ")
+            print(pose)
+
+            depth_shift = 1000.0
+            x, y = np.meshgrid(
+                np.linspace(0, depth_img.shape[1] - 1, depth_img.shape[1]),
+                np.linspace(0, depth_img.shape[0] - 1, depth_img.shape[0]),
+            )
+            uv_depth = np.zeros((depth_img.shape[0], depth_img.shape[1], 3))
+            uv_depth[:, :, 0] = x
+            uv_depth[:, :, 1] = y
+            uv_depth[:, :, 2] = depth_img / depth_shift
+            uv_depth = np.reshape(uv_depth, [-1, 3])
+            uv_depth = uv_depth[np.where(uv_depth[:, 2] != 0), :].squeeze()
+
+            intrinsic_inv = np.linalg.inv(depth_intrinsic)
+            fx = depth_intrinsic[0, 0]
+            fy = depth_intrinsic[1, 1]
+            cx = depth_intrinsic[0, 2]
+            cy = depth_intrinsic[1, 2]
+            bx = depth_intrinsic[0, 3]
+            by = depth_intrinsic[1, 3]
+            point_list = []
+            n = uv_depth.shape[0]
+            points = np.ones((n, 4))
+            X = (uv_depth[:, 0] - cx) * uv_depth[:, 2] / fx + bx
+            Y = (uv_depth[:, 1] - cy) * uv_depth[:, 2] / fy + by
+            points[:, 0] = X
+            points[:, 1] = Y
+            points[:, 2] = uv_depth[:, 2]
+            points_world = np.dot(points, np.transpose(pose))
+            print(points_world.shape)
+
+            pcd = dict(coord=points_world[:, :3], color=colors)
+            # pcd_save = np.zeros((points_world.shape[0], 7))
+            # pcd_save[:, :3] = points_world[:, :3]
+            # pcd_save[:, 3:6] = colors
+
+            # print('Saving npz file...')
+            # np.savez(output_path + '/{}.npz'.format(name), pcd=pcd_save)
+            torch.save(pcd, output_path + "/{}.pth".format(name))
+        except:
+            continue

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/preprocess.py

@@ -0,0 +1,51 @@
+import os
+import argparse
+import glob
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from reader import reader
+from point_cloud_extractor import extractor
+from compute_full_overlapping import compute_full_overlapping
+
+
+frame_skip = 25
+
+
+def parse_sens(sens_dir, output_dir):
+    scene_id = os.path.basename(os.path.dirname(sens_dir))
+    print(f"Parsing sens data{sens_dir}")
+    reader(
+        sens_dir,
+        os.path.join(output_dir, scene_id),
+        frame_skip,
+        export_color_images=True,
+        export_depth_images=True,
+        export_poses=True,
+        export_intrinsics=True,
+    )
+    extractor(
+        os.path.join(output_dir, scene_id), os.path.join(output_dir, scene_id, "pcd")
+    )
+    compute_full_overlapping(output_dir, scene_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    opt = parser.parse_args()
+    sens_list = sorted(glob.glob(os.path.join(opt.dataset_root, "scans/scene*/*.sens")))
+    # Preprocess data.
+    pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    # pool = ProcessPoolExecutor(max_workers=1)
+    print("Processing scenes...")
+    _ = list(pool.map(parse_sens, sens_list, repeat(opt.output_root)))

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannet/scannet_pair/reader.py

@@ -0,0 +1,33 @@
+import argparse
+import os, sys
+
+from SensorData import SensorData
+
+
+def reader(
+    filename,
+    output_path,
+    frame_skip,
+    export_color_images=False,
+    export_depth_images=False,
+    export_poses=False,
+    export_intrinsics=False,
+):
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+
+    # load the data
+    print("loading %s..." % filename)
+    sd = SensorData(filename)
+    if export_depth_images:
+        sd.export_depth_images(
+            os.path.join(output_path, "depth"), frame_skip=frame_skip
+        )
+    if export_color_images:
+        sd.export_color_images(
+            os.path.join(output_path, "color"), frame_skip=frame_skip
+        )
+    if export_poses:
+        sd.export_poses(os.path.join(output_path, "pose"), frame_skip=frame_skip)
+    if export_intrinsics:
+        sd.export_intrinsics(os.path.join(output_path, "intrinsic"))

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/scannetpp/preprocess_scannetpp.py

@@ -0,0 +1,252 @@
+"""
+Preprocessing Script for ScanNet++
+modified from official preprocess code.
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import open3d as o3d
+import multiprocessing as mp
+from collections import OrderedDict
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+from pathlib import Path
+
+
+def parse_scene(
+    name,
+    split,
+    dataset_root,
+    output_root,
+    label_mapping,
+    class2idx,
+    ignore_index=-1,
+):
+    print(f"Parsing scene {name} in {split} split")
+    dataset_root = Path(dataset_root)
+    output_root = Path(output_root)
+    scene_path = dataset_root / "data" / name / "scans"
+    mesh_path = scene_path / "mesh_aligned_0.05.ply"
+    segs_path = scene_path / "segments.json"
+    anno_path = scene_path / "segments_anno.json"
+
+    # load mesh vertices and colors
+    mesh = o3d.io.read_triangle_mesh(str(mesh_path))
+
+    # extract mesh information
+    mesh.compute_vertex_normals(normalized=True)
+    coord = np.array(mesh.vertices).astype(np.float32)
+    color = (np.array(mesh.vertex_colors) * 255).astype(np.uint8)
+    normal = np.array(mesh.vertex_normals).astype(np.float32)
+
+    save_path = output_root / split / name
+    save_path.mkdir(parents=True, exist_ok=True)
+    np.save(save_path / "coord.npy", coord)
+    np.save(save_path / "color.npy", color)
+    np.save(save_path / "normal.npy", normal)
+
+    if split == "test":
+        return
+
+    # get label on vertices
+    # load segments = vertices per segment ID
+    with open(segs_path) as f:
+        segments = json.load(f)
+    # load anno = (instance, groups of segments)
+    with open(anno_path) as f:
+        anno = json.load(f)
+    seg_indices = np.array(segments["segIndices"], dtype=np.uint32)
+    num_vertices = len(seg_indices)
+    assert num_vertices == len(coord)
+    semantic_gt = np.ones((num_vertices, 3), dtype=np.int16) * ignore_index
+    instance_gt = np.ones((num_vertices, 3), dtype=np.int16) * ignore_index
+
+    # number of labels are used per vertex. initially 0
+    # increment each time a new label is added
+    instance_size = np.ones((num_vertices, 3), dtype=np.int16) * np.inf
+
+    # keep track of the size of the instance (#vertices) assigned to each vertex
+    # later, keep the label of the smallest instance for major label of vertices
+    # store inf initially so that we can pick the smallest instance
+    labels_used = np.zeros(num_vertices, dtype=np.int16)
+
+    for idx, instance in enumerate(anno["segGroups"]):
+        label = instance["label"]
+        instance["label_orig"] = label
+        # remap label
+        instance["label"] = label_mapping.get(label, None)
+        instance["label_index"] = class2idx.get(label, ignore_index)
+
+        if instance["label_index"] == ignore_index:
+            continue
+        # get all the vertices with segment index in this instance
+        # and max number of labels not yet applied
+        mask = np.isin(seg_indices, instance["segments"]) & (labels_used < 3)
+        size = mask.sum()
+        if size == 0:
+            continue
+
+        # get the position to add the label - 0, 1, 2
+        label_position = labels_used[mask]
+        semantic_gt[mask, label_position] = instance["label_index"]
+        # store all valid instance (include ignored instance)
+        instance_gt[mask, label_position] = instance["objectId"]
+        instance_size[mask, label_position] = size
+        labels_used[mask] += 1
+
+    # major label is the label of smallest instance for each vertex
+    # use major label for single class segmentation
+    # shift major label to the first column
+    mask = labels_used > 1
+    if mask.sum() > 0:
+        major_label_position = np.argmin(instance_size[mask], axis=1)
+
+        major_semantic_label = semantic_gt[mask, major_label_position]
+        semantic_gt[mask, major_label_position] = semantic_gt[:, 0][mask]
+        semantic_gt[:, 0][mask] = major_semantic_label
+
+        major_instance_label = instance_gt[mask, major_label_position]
+        instance_gt[mask, major_label_position] = instance_gt[:, 0][mask]
+        instance_gt[:, 0][mask] = major_instance_label
+
+    np.save(save_path / "segment.npy", semantic_gt)
+    np.save(save_path / "instance.npy", instance_gt)
+
+
+def filter_map_classes(mapping, count_thresh, count_type, mapping_type):
+    mapping = mapping[mapping[count_type] >= count_thresh]
+    if mapping_type == "semantic":
+        map_key = "semantic_map_to"
+    elif mapping_type == "instance":
+        map_key = "instance_map_to"
+    else:
+        raise NotImplementedError
+    # create a dict with classes to be mapped
+    # classes that don't have mapping are entered as x->x
+    # otherwise x->y
+    map_dict = OrderedDict()
+
+    for i in range(mapping.shape[0]):
+        row = mapping.iloc[i]
+        class_name = row["class"]
+        map_target = row[map_key]
+
+        # map to None or some other label -> don't add this class to the label list
+        try:
+            if len(map_target) > 0:
+                # map to None -> don't use this class
+                if map_target == "None":
+                    pass
+                else:
+                    # map to something else -> use this class
+                    map_dict[class_name] = map_target
+        except TypeError:
+            # nan values -> no mapping, keep label as is
+            if class_name not in map_dict:
+                map_dict[class_name] = class_name
+
+    return map_dict
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet++ dataset containing data/metadata/splits.",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val/test folders will be located.",
+    )
+    parser.add_argument(
+        "--ignore_index",
+        default=-1,
+        type=int,
+        help="Default ignore index.",
+    )
+    parser.add_argument(
+        "--num_workers",
+        default=mp.cpu_count(),
+        type=int,
+        help="Num workers for preprocessing.",
+    )
+    config = parser.parse_args()
+
+    print("Loading meta data...")
+    config.dataset_root = Path(config.dataset_root)
+    config.output_root = Path(config.output_root)
+
+    train_list = np.loadtxt(
+        config.dataset_root / "splits" / "nvs_sem_train.txt",
+        dtype=str,
+    )
+    print("Num samples in training split:", len(train_list))
+
+    val_list = np.loadtxt(
+        config.dataset_root / "splits" / "nvs_sem_val.txt",
+        dtype=str,
+    )
+    print("Num samples in validation split:", len(val_list))
+
+    test_list = np.loadtxt(
+        config.dataset_root / "splits" / "sem_test.txt",
+        dtype=str,
+    )
+    print("Num samples in testing split:", len(test_list))
+
+    data_list = np.concatenate([train_list, val_list, test_list])
+    split_list = np.concatenate(
+        [
+            np.full_like(train_list, "train"),
+            np.full_like(val_list, "val"),
+            np.full_like(test_list, "test"),
+        ]
+    )
+
+    # Parsing label information and mapping
+    segment_class_names = np.loadtxt(
+        config.dataset_root / "metadata" / "semantic_benchmark" / "top100.txt",
+        dtype=str,
+        delimiter=".",  # dummy delimiter to replace " "
+    )
+    print("Num classes in segment class list:", len(segment_class_names))
+
+    instance_class_names = np.loadtxt(
+        config.dataset_root / "metadata" / "semantic_benchmark" / "top100_instance.txt",
+        dtype=str,
+        delimiter=".",  # dummy delimiter to replace " "
+    )
+    print("Num classes in instance class list:", len(instance_class_names))
+
+    label_mapping = pd.read_csv(
+        config.dataset_root / "metadata" / "semantic_benchmark" / "map_benchmark.csv"
+    )
+    label_mapping = filter_map_classes(
+        label_mapping, count_thresh=0, count_type="count", mapping_type="semantic"
+    )
+    class2idx = {
+        class_name: idx for (idx, class_name) in enumerate(segment_class_names)
+    }
+
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=config.num_workers)
+    _ = list(
+        pool.map(
+            parse_scene,
+            data_list,
+            split_list,
+            repeat(config.dataset_root),
+            repeat(config.output_root),
+            repeat(label_mapping),
+            repeat(class2idx),
+            repeat(config.ignore_index),
+        )
+    )
+    pool.shutdown()

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/structured3d/preprocess_structured3d.py

@@ -0,0 +1,420 @@
+"""
+Preprocessing Script for Structured3D
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import argparse
+import io
+import os
+import PIL
+from PIL import Image
+import cv2
+import zipfile
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+
+VALID_CLASS_IDS_25 = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    11,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    22,
+    24,
+    25,
+    32,
+    34,
+    35,
+    38,
+    39,
+    40,
+)
+CLASS_LABELS_25 = (
+    "wall",
+    "floor",
+    "cabinet",
+    "bed",
+    "chair",
+    "sofa",
+    "table",
+    "door",
+    "window",
+    "picture",
+    "desk",
+    "shelves",
+    "curtain",
+    "dresser",
+    "pillow",
+    "mirror",
+    "ceiling",
+    "refrigerator",
+    "television",
+    "nightstand",
+    "sink",
+    "lamp",
+    "otherstructure",
+    "otherfurniture",
+    "otherprop",
+)
+
+
+def normal_from_cross_product(points_2d: np.ndarray) -> np.ndarray:
+    xyz_points_pad = np.pad(points_2d, ((0, 1), (0, 1), (0, 0)), mode="symmetric")
+    xyz_points_ver = (xyz_points_pad[:, :-1, :] - xyz_points_pad[:, 1:, :])[:-1, :, :]
+    xyz_points_hor = (xyz_points_pad[:-1, :, :] - xyz_points_pad[1:, :, :])[:, :-1, :]
+    xyz_normal = np.cross(xyz_points_hor, xyz_points_ver)
+    xyz_dist = np.linalg.norm(xyz_normal, axis=-1, keepdims=True)
+    xyz_normal = np.divide(
+        xyz_normal, xyz_dist, out=np.zeros_like(xyz_normal), where=xyz_dist != 0
+    )
+    return xyz_normal
+
+
+class Structured3DReader:
+    def __init__(self, files):
+        super().__init__()
+        if isinstance(files, str):
+            files = [files]
+        self.readers = [zipfile.ZipFile(f, "r") for f in files]
+        self.names_mapper = dict()
+        for idx, reader in enumerate(self.readers):
+            for name in reader.namelist():
+                self.names_mapper[name] = idx
+
+    def filelist(self):
+        return list(self.names_mapper.keys())
+
+    def listdir(self, dir_name):
+        dir_name = dir_name.lstrip(os.path.sep).rstrip(os.path.sep)
+        file_list = list(
+            np.unique(
+                [
+                    f.replace(dir_name + os.path.sep, "", 1).split(os.path.sep)[0]
+                    for f in self.filelist()
+                    if f.startswith(dir_name + os.path.sep)
+                ]
+            )
+        )
+        if "" in file_list:
+            file_list.remove("")
+        return file_list
+
+    def read(self, file_name):
+        split = self.names_mapper[file_name]
+        return self.readers[split].read(file_name)
+
+    def read_camera(self, camera_path):
+        z2y_top_m = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=np.float32)
+        cam_extr = np.fromstring(self.read(camera_path), dtype=np.float32, sep=" ")
+        cam_t = np.matmul(z2y_top_m, cam_extr[:3] / 1000)
+        if cam_extr.shape[0] > 3:
+            cam_front, cam_up = cam_extr[3:6], cam_extr[6:9]
+            cam_n = np.cross(cam_front, cam_up)
+            cam_r = np.stack((cam_front, cam_up, cam_n), axis=1).astype(np.float32)
+            cam_r = np.matmul(z2y_top_m, cam_r)
+            cam_f = cam_extr[9:11]
+        else:
+            cam_r = np.eye(3, dtype=np.float32)
+            cam_f = None
+        return cam_r, cam_t, cam_f
+
+    def read_depth(self, depth_path):
+        depth = cv2.imdecode(
+            np.frombuffer(self.read(depth_path), np.uint8), cv2.IMREAD_UNCHANGED
+        )[..., np.newaxis]
+        depth[depth == 0] = 65535
+        return depth
+
+    def read_color(self, color_path):
+        color = cv2.imdecode(
+            np.frombuffer(self.read(color_path), np.uint8), cv2.IMREAD_UNCHANGED
+        )[..., :3][..., ::-1]
+        return color
+
+    def read_segment(self, segment_path):
+        segment = np.array(PIL.Image.open(io.BytesIO(self.read(segment_path))))[
+            ..., np.newaxis
+        ]
+        return segment
+
+
+def parse_scene(
+    scene,
+    dataset_root,
+    output_root,
+    ignore_index=-1,
+    grid_size=None,
+    fuse_prsp=True,
+    fuse_pano=True,
+    vis=False,
+):
+    assert fuse_prsp or fuse_pano
+    reader = Structured3DReader(
+        [
+            os.path.join(dataset_root, f)
+            for f in os.listdir(dataset_root)
+            if f.endswith(".zip")
+        ]
+    )
+    scene_id = int(os.path.basename(scene).split("_")[-1])
+    if scene_id < 3000:
+        split = "train"
+    elif 3000 <= scene_id < 3250:
+        split = "val"
+    else:
+        split = "test"
+
+    print(f"Processing: {scene} in {split}")
+    rooms = reader.listdir(os.path.join("Structured3D", scene, "2D_rendering"))
+    for room in rooms:
+        room_path = os.path.join("Structured3D", scene, "2D_rendering", room)
+        coord_list = list()
+        color_list = list()
+        normal_list = list()
+        segment_list = list()
+        if fuse_prsp:
+            prsp_path = os.path.join(room_path, "perspective", "full")
+            frames = reader.listdir(prsp_path)
+
+            for frame in frames:
+                try:
+                    cam_r, cam_t, cam_f = reader.read_camera(
+                        os.path.join(prsp_path, frame, "camera_pose.txt")
+                    )
+                    depth = reader.read_depth(
+                        os.path.join(prsp_path, frame, "depth.png")
+                    )
+                    color = reader.read_color(
+                        os.path.join(prsp_path, frame, "rgb_rawlight.png")
+                    )
+                    segment = reader.read_segment(
+                        os.path.join(prsp_path, frame, "semantic.png")
+                    )
+                except:
+                    print(
+                        f"Skipping {scene}_room{room}_frame{frame} perspective view due to loading error"
+                    )
+                else:
+                    fx, fy = cam_f
+                    height, width = depth.shape[0], depth.shape[1]
+                    pixel = np.transpose(np.indices((width, height)), (2, 1, 0))
+                    pixel = pixel.reshape((-1, 2))
+                    pixel = np.hstack((pixel, np.ones((pixel.shape[0], 1))))
+                    k = np.diag([1.0, 1.0, 1.0])
+
+                    k[0, 2] = width / 2
+                    k[1, 2] = height / 2
+
+                    k[0, 0] = k[0, 2] / np.tan(fx)
+                    k[1, 1] = k[1, 2] / np.tan(fy)
+                    coord = (
+                        depth.reshape((-1, 1)) * (np.linalg.inv(k) @ pixel.T).T
+                    ).reshape(height, width, 3)
+                    coord = coord @ np.array([[0, 0, 1], [0, -1, 0], [1, 0, 0]])
+                    normal = normal_from_cross_product(coord)
+
+                    # Filtering invalid points
+                    view_dist = np.maximum(
+                        np.linalg.norm(coord, axis=-1, keepdims=True), float(10e-5)
+                    )
+                    cosine_dist = np.sum(
+                        (coord * normal / view_dist), axis=-1, keepdims=True
+                    )
+                    cosine_dist = np.abs(cosine_dist)
+                    mask = ((cosine_dist > 0.15) & (depth < 65535) & (segment > 0))[
+                        ..., 0
+                    ].reshape(-1)
+
+                    coord = np.matmul(coord / 1000, cam_r.T) + cam_t
+                    normal = normal_from_cross_product(coord)
+
+                    if sum(mask) > 0:
+                        coord_list.append(coord.reshape(-1, 3)[mask])
+                        color_list.append(color.reshape(-1, 3)[mask])
+                        normal_list.append(normal.reshape(-1, 3)[mask])
+                        segment_list.append(segment.reshape(-1, 1)[mask])
+                    else:
+                        print(
+                            f"Skipping {scene}_room{room}_frame{frame} perspective view due to all points are filtered out"
+                        )
+
+        if fuse_pano:
+            pano_path = os.path.join(room_path, "panorama")
+            try:
+                _, cam_t, _ = reader.read_camera(
+                    os.path.join(pano_path, "camera_xyz.txt")
+                )
+                depth = reader.read_depth(os.path.join(pano_path, "full", "depth.png"))
+                color = reader.read_color(
+                    os.path.join(pano_path, "full", "rgb_rawlight.png")
+                )
+                segment = reader.read_segment(
+                    os.path.join(pano_path, "full", "semantic.png")
+                )
+            except:
+                print(f"Skipping {scene}_room{room} panorama view due to loading error")
+            else:
+                p_h, p_w = depth.shape[:2]
+                p_a = np.arange(p_w, dtype=np.float32) / p_w * 2 * np.pi - np.pi
+                p_b = np.arange(p_h, dtype=np.float32) / p_h * np.pi * -1 + np.pi / 2
+                p_a = np.tile(p_a[None], [p_h, 1])[..., np.newaxis]
+                p_b = np.tile(p_b[:, None], [1, p_w])[..., np.newaxis]
+                p_a_sin, p_a_cos, p_b_sin, p_b_cos = (
+                    np.sin(p_a),
+                    np.cos(p_a),
+                    np.sin(p_b),
+                    np.cos(p_b),
+                )
+                x = depth * p_a_cos * p_b_cos
+                y = depth * p_b_sin
+                z = depth * p_a_sin * p_b_cos
+                coord = np.concatenate([x, y, z], axis=-1) / 1000
+                normal = normal_from_cross_product(coord)
+
+                # Filtering invalid points
+                view_dist = np.maximum(
+                    np.linalg.norm(coord, axis=-1, keepdims=True), float(10e-5)
+                )
+                cosine_dist = np.sum(
+                    (coord * normal / view_dist), axis=-1, keepdims=True
+                )
+                cosine_dist = np.abs(cosine_dist)
+                mask = ((cosine_dist > 0.15) & (depth < 65535) & (segment > 0))[
+                    ..., 0
+                ].reshape(-1)
+                coord = coord + cam_t
+
+                if sum(mask) > 0:
+                    coord_list.append(coord.reshape(-1, 3)[mask])
+                    color_list.append(color.reshape(-1, 3)[mask])
+                    normal_list.append(normal.reshape(-1, 3)[mask])
+                    segment_list.append(segment.reshape(-1, 1)[mask])
+                else:
+                    print(
+                        f"Skipping {scene}_room{room} panorama view due to all points are filtered out"
+                    )
+
+        if len(coord_list) > 0:
+            coord = np.concatenate(coord_list, axis=0)
+            coord = coord @ np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+            color = np.concatenate(color_list, axis=0)
+            normal = np.concatenate(normal_list, axis=0)
+            normal = normal @ np.array([[1, 0, 0], [0, 0, 1], [0, 1, 0]])
+            segment = np.concatenate(segment_list, axis=0)
+            segment25 = np.ones_like(segment, dtype=np.int64) * ignore_index
+            for idx, value in enumerate(VALID_CLASS_IDS_25):
+                mask = np.all(segment == value, axis=-1)
+                segment25[mask] = idx
+
+            data_dict = dict(
+                coord=coord.astype(np.float32),
+                color=color.astype(np.uint8),
+                normal=normal.astype(np.float32),
+                segment=segment25.astype(np.int16),
+            )
+            # Grid sampling data
+            if grid_size is not None:
+                grid_coord = np.floor(coord / grid_size).astype(int)
+                _, idx = np.unique(grid_coord, axis=0, return_index=True)
+                coord = coord[idx]
+                for key in data_dict.keys():
+                    data_dict[key] = data_dict[key][idx]
+
+            # Save data
+            save_path = os.path.join(
+                output_root, split, os.path.basename(scene), f"room_{room}"
+            )
+            os.makedirs(save_path, exist_ok=True)
+            for key in data_dict.keys():
+                np.save(os.path.join(save_path, f"{key}.npy"), data_dict[key])
+
+            if vis:
+                from pointcept.utils.visualization import save_point_cloud
+
+                os.makedirs("./vis", exist_ok=True)
+                save_point_cloud(
+                    coord, color / 255, f"./vis/{scene}_room{room}_color.ply"
+                )
+                save_point_cloud(
+                    coord, (normal + 1) / 2, f"./vis/{scene}_room{room}_normal.ply"
+                )
+        else:
+            print(f"Skipping {scene}_room{room} due to no valid points")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders.",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located.",
+    )
+    parser.add_argument(
+        "--num_workers",
+        default=mp.cpu_count(),
+        type=int,
+        help="Num workers for preprocessing.",
+    )
+    parser.add_argument(
+        "--grid_size", default=None, type=float, help="Grid size for grid sampling."
+    )
+    parser.add_argument("--ignore_index", default=-1, type=float, help="Ignore index.")
+    parser.add_argument(
+        "--fuse_prsp", action="store_true", help="Whether fuse perspective view."
+    )
+    parser.add_argument(
+        "--fuse_pano", action="store_true", help="Whether fuse panorama view."
+    )
+    config = parser.parse_args()
+
+    reader = Structured3DReader(
+        [
+            os.path.join(config.dataset_root, f)
+            for f in os.listdir(config.dataset_root)
+            if f.endswith(".zip")
+        ]
+    )
+
+    scenes_list = reader.listdir("Structured3D")
+    scenes_list = sorted(scenes_list)
+    os.makedirs(os.path.join(config.output_root, "train"), exist_ok=True)
+    os.makedirs(os.path.join(config.output_root, "val"), exist_ok=True)
+    os.makedirs(os.path.join(config.output_root, "test"), exist_ok=True)
+
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=config.num_workers)
+    _ = list(
+        pool.map(
+            parse_scene,
+            scenes_list,
+            repeat(config.dataset_root),
+            repeat(config.output_root),
+            repeat(config.ignore_index),
+            repeat(config.grid_size),
+            repeat(config.fuse_prsp),
+            repeat(config.fuse_pano),
+        )
+    )
+    pool.shutdown()

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/preprocessing/waymo/preprocess_waymo.py

@@ -0,0 +1,387 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+import argparse
+import numpy as np
+import tensorflow.compat.v1 as tf
+from pathlib import Path
+from waymo_open_dataset.utils import frame_utils
+from waymo_open_dataset.utils import transform_utils
+from waymo_open_dataset.utils import range_image_utils
+from waymo_open_dataset import dataset_pb2 as open_dataset
+import glob
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+
+def create_lidar(frame):
+    """Parse and save the lidar data in psd format.
+    Args:
+        frame (:obj:`Frame`): Open dataset frame proto.
+    """
+    (
+        range_images,
+        camera_projections,
+        segmentation_labels,
+        range_image_top_pose,
+    ) = frame_utils.parse_range_image_and_camera_projection(frame)
+
+    points, cp_points, valid_masks = convert_range_image_to_point_cloud(
+        frame,
+        range_images,
+        camera_projections,
+        range_image_top_pose,
+        keep_polar_features=True,
+    )
+    points_ri2, cp_points_ri2, valid_masks_ri2 = convert_range_image_to_point_cloud(
+        frame,
+        range_images,
+        camera_projections,
+        range_image_top_pose,
+        ri_index=1,
+        keep_polar_features=True,
+    )
+
+    # 3d points in vehicle frame.
+    points_all = np.concatenate(points, axis=0)
+    points_all_ri2 = np.concatenate(points_ri2, axis=0)
+    # point labels.
+
+    points_all = np.concatenate([points_all, points_all_ri2], axis=0)
+
+    velodyne = np.c_[points_all[:, 3:6], points_all[:, 1]]
+    velodyne = velodyne.reshape((velodyne.shape[0] * velodyne.shape[1]))
+
+    valid_masks = [valid_masks, valid_masks_ri2]
+    return velodyne, valid_masks
+
+
+def create_label(frame):
+    (
+        range_images,
+        camera_projections,
+        segmentation_labels,
+        range_image_top_pose,
+    ) = frame_utils.parse_range_image_and_camera_projection(frame)
+
+    point_labels = convert_range_image_to_point_cloud_labels(
+        frame, range_images, segmentation_labels
+    )
+    point_labels_ri2 = convert_range_image_to_point_cloud_labels(
+        frame, range_images, segmentation_labels, ri_index=1
+    )
+
+    # point labels.
+    point_labels_all = np.concatenate(point_labels, axis=0)
+    point_labels_all_ri2 = np.concatenate(point_labels_ri2, axis=0)
+    point_labels_all = np.concatenate([point_labels_all, point_labels_all_ri2], axis=0)
+
+    labels = point_labels_all
+    return labels
+
+
+def convert_range_image_to_cartesian(
+    frame, range_images, range_image_top_pose, ri_index=0, keep_polar_features=False
+):
+    """Convert range images from polar coordinates to Cartesian coordinates.
+
+    Args:
+      frame: open dataset frame
+      range_images: A dict of {laser_name, [range_image_first_return,
+        range_image_second_return]}.
+      range_image_top_pose: range image pixel pose for top lidar.
+      ri_index: 0 for the first return, 1 for the second return.
+      keep_polar_features: If true, keep the features from the polar range image
+        (i.e. range, intensity, and elongation) as the first features in the
+        output range image.
+
+    Returns:
+      dict of {laser_name, (H, W, D)} range images in Cartesian coordinates. D
+        will be 3 if keep_polar_features is False (x, y, z) and 6 if
+        keep_polar_features is True (range, intensity, elongation, x, y, z).
+    """
+    cartesian_range_images = {}
+    frame_pose = tf.convert_to_tensor(
+        value=np.reshape(np.array(frame.pose.transform), [4, 4])
+    )
+
+    # [H, W, 6]
+    range_image_top_pose_tensor = tf.reshape(
+        tf.convert_to_tensor(value=range_image_top_pose.data),
+        range_image_top_pose.shape.dims,
+    )
+    # [H, W, 3, 3]
+    range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(
+        range_image_top_pose_tensor[..., 0],
+        range_image_top_pose_tensor[..., 1],
+        range_image_top_pose_tensor[..., 2],
+    )
+    range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]
+    range_image_top_pose_tensor = transform_utils.get_transform(
+        range_image_top_pose_tensor_rotation, range_image_top_pose_tensor_translation
+    )
+
+    for c in frame.context.laser_calibrations:
+        range_image = range_images[c.name][ri_index]
+        if len(c.beam_inclinations) == 0:  # pylint: disable=g-explicit-length-test
+            beam_inclinations = range_image_utils.compute_inclination(
+                tf.constant([c.beam_inclination_min, c.beam_inclination_max]),
+                height=range_image.shape.dims[0],
+            )
+        else:
+            beam_inclinations = tf.constant(c.beam_inclinations)
+
+        beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])
+        extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])
+
+        range_image_tensor = tf.reshape(
+            tf.convert_to_tensor(value=range_image.data), range_image.shape.dims
+        )
+        pixel_pose_local = None
+        frame_pose_local = None
+        if c.name == open_dataset.LaserName.TOP:
+            pixel_pose_local = range_image_top_pose_tensor
+            pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)
+            frame_pose_local = tf.expand_dims(frame_pose, axis=0)
+        range_image_cartesian = range_image_utils.extract_point_cloud_from_range_image(
+            tf.expand_dims(range_image_tensor[..., 0], axis=0),
+            tf.expand_dims(extrinsic, axis=0),
+            tf.expand_dims(tf.convert_to_tensor(value=beam_inclinations), axis=0),
+            pixel_pose=pixel_pose_local,
+            frame_pose=frame_pose_local,
+        )
+
+        range_image_cartesian = tf.squeeze(range_image_cartesian, axis=0)
+
+        if keep_polar_features:
+            # If we want to keep the polar coordinate features of range, intensity,
+            # and elongation, concatenate them to be the initial dimensions of the
+            # returned Cartesian range image.
+            range_image_cartesian = tf.concat(
+                [range_image_tensor[..., 0:3], range_image_cartesian], axis=-1
+            )
+
+        cartesian_range_images[c.name] = range_image_cartesian
+
+    return cartesian_range_images
+
+
+def convert_range_image_to_point_cloud(
+    frame,
+    range_images,
+    camera_projections,
+    range_image_top_pose,
+    ri_index=0,
+    keep_polar_features=False,
+):
+    """Convert range images to point cloud.
+
+    Args:
+      frame: open dataset frame
+      range_images: A dict of {laser_name, [range_image_first_return,
+        range_image_second_return]}.
+      camera_projections: A dict of {laser_name,
+        [camera_projection_from_first_return,
+        camera_projection_from_second_return]}.
+      range_image_top_pose: range image pixel pose for top lidar.
+      ri_index: 0 for the first return, 1 for the second return.
+      keep_polar_features: If true, keep the features from the polar range image
+        (i.e. range, intensity, and elongation) as the first features in the
+        output range image.
+
+    Returns:
+      points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).
+        (NOTE: Will be {[N, 6]} if keep_polar_features is true.
+      cp_points: {[N, 6]} list of camera projections of length 5
+        (number of lidars).
+    """
+    calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+    points = []
+    cp_points = []
+    valid_masks = []
+
+    cartesian_range_images = convert_range_image_to_cartesian(
+        frame, range_images, range_image_top_pose, ri_index, keep_polar_features
+    )
+
+    for c in calibrations:
+        range_image = range_images[c.name][ri_index]
+        range_image_tensor = tf.reshape(
+            tf.convert_to_tensor(value=range_image.data), range_image.shape.dims
+        )
+        range_image_mask = range_image_tensor[..., 0] > 0
+
+        range_image_cartesian = cartesian_range_images[c.name]
+        points_tensor = tf.gather_nd(
+            range_image_cartesian, tf.compat.v1.where(range_image_mask)
+        )
+
+        cp = camera_projections[c.name][ri_index]
+        cp_tensor = tf.reshape(tf.convert_to_tensor(value=cp.data), cp.shape.dims)
+        cp_points_tensor = tf.gather_nd(cp_tensor, tf.compat.v1.where(range_image_mask))
+        points.append(points_tensor.numpy())
+        cp_points.append(cp_points_tensor.numpy())
+        valid_masks.append(range_image_mask.numpy())
+
+    return points, cp_points, valid_masks
+
+
+def convert_range_image_to_point_cloud_labels(
+    frame, range_images, segmentation_labels, ri_index=0
+):
+    """Convert segmentation labels from range images to point clouds.
+
+    Args:
+    frame: open dataset frame
+    range_images: A dict of {laser_name, [range_image_first_return,
+    range_image_second_return]}.
+    segmentation_labels: A dict of {laser_name, [range_image_first_return,
+    range_image_second_return]}.
+    ri_index: 0 for the first return, 1 for the second return.
+
+    Returns:
+    point_labels: {[N, 2]} list of 3d lidar points's segmentation labels. 0 for
+    points that are not labeled.
+    """
+    calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)
+    point_labels = []
+    for c in calibrations:
+        range_image = range_images[c.name][ri_index]
+        range_image_tensor = tf.reshape(
+            tf.convert_to_tensor(range_image.data), range_image.shape.dims
+        )
+        range_image_mask = range_image_tensor[..., 0] > 0
+
+        if c.name in segmentation_labels:
+            sl = segmentation_labels[c.name][ri_index]
+            sl_tensor = tf.reshape(tf.convert_to_tensor(sl.data), sl.shape.dims)
+            sl_points_tensor = tf.gather_nd(sl_tensor, tf.where(range_image_mask))
+        else:
+            num_valid_point = tf.math.reduce_sum(tf.cast(range_image_mask, tf.int32))
+            sl_points_tensor = tf.zeros([num_valid_point, 2], dtype=tf.int32)
+
+        point_labels.append(sl_points_tensor.numpy())
+    return point_labels
+
+
+def handle_process(file_path, output_root, test_frame_list):
+    file = os.path.basename(file_path)
+    split = os.path.basename(os.path.dirname(file_path))
+    print(f"Parsing {split}/{file}")
+    save_path = Path(output_root) / split / file.split(".")[0]
+
+    data_group = tf.data.TFRecordDataset(file_path, compression_type="")
+    for data in data_group:
+        frame = open_dataset.Frame()
+        frame.ParseFromString(bytearray(data.numpy()))
+        context_name = frame.context.name
+        timestamp = str(frame.timestamp_micros)
+
+        if split != "testing":
+            # for training and validation frame, extract labelled frame
+            if not frame.lasers[0].ri_return1.segmentation_label_compressed:
+                continue
+        else:
+            # for testing frame, extract frame in test_frame_list
+            if f"{context_name},{timestamp}" not in test_frame_list:
+                continue
+
+        os.makedirs(save_path / timestamp, exist_ok=True)
+
+        # extract frame pass above check
+        point_cloud, valid_masks = create_lidar(frame)
+        point_cloud = point_cloud.reshape(-1, 4)
+        coord = point_cloud[:, :3]
+        strength = np.tanh(point_cloud[:, -1].reshape([-1, 1]))
+        pose = np.array(frame.pose.transform, np.float32).reshape(4, 4)
+        mask = np.array(valid_masks, dtype=object)
+
+        np.save(save_path / timestamp / "coord.npy", coord)
+        np.save(save_path / timestamp / "strength.npy", strength)
+        np.save(save_path / timestamp / "pose.npy", pose)
+
+        # save mask for reverse prediction
+        if split != "training":
+            np.save(save_path / timestamp / "mask.npy", mask)
+
+        # save label
+        if split != "testing":
+            # ignore TYPE_UNDEFINED, ignore_index 0 -> -1
+            label = create_label(frame)[:, 1].reshape([-1]) - 1
+            np.save(save_path / timestamp / "segment.npy", label)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the Waymo dataset",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    parser.add_argument(
+        "--splits",
+        required=True,
+        nargs="+",
+        choices=["training", "validation", "testing"],
+        help="Splits need to process ([training, validation, testing]).",
+    )
+    parser.add_argument(
+        "--num_workers",
+        default=mp.cpu_count(),
+        type=int,
+        help="Num workers for preprocessing.",
+    )
+    config = parser.parse_args()
+
+    # load file list
+    file_list = glob.glob(
+        os.path.join(os.path.abspath(config.dataset_root), "*", "*.tfrecord")
+    )
+    assert len(file_list) == 1150
+
+    # Create output directories
+    for split in config.splits:
+        os.makedirs(os.path.join(config.output_root, split), exist_ok=True)
+
+    file_list = [
+        file
+        for file in file_list
+        if os.path.basename(os.path.dirname(file)) in config.splits
+    ]
+
+    # Load test frame list
+    test_frame_file = os.path.join(
+        os.path.dirname(__file__), "3d_semseg_test_set_frames.txt"
+    )
+    test_frame_list = [x.rstrip() for x in (open(test_frame_file, "r").readlines())]
+
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=config.num_workers)
+    _ = list(
+        pool.map(
+            handle_process,
+            file_list,
+            repeat(config.output_root),
+            repeat(test_frame_list),
+        )
+    )

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/s3dis.py

@@ -0,0 +1,18 @@
+"""
+S3DIS Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from .defaults import DefaultDataset
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class S3DISDataset(DefaultDataset):
+    def get_data_name(self, idx):
+        remain, room_name = os.path.split(self.data_list[idx % len(self.data_list)])
+        remain, area_name = os.path.split(remain)
+        return f"{area_name}-{room_name}"

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet.py

@@ -0,0 +1,290 @@
+"""
+ScanNet20 / ScanNet200 / ScanNet Data Efficient Dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import glob
+import os
+from collections.abc import Sequence
+from copy import deepcopy
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from pointcept.utils.cache import shared_dict
+from pointcept.utils.logger import get_root_logger
+
+from .builder import DATASETS
+from .defaults import DefaultDatasetV2
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_20, VALID_CLASS_IDS_200
+from .transform import TRANSFORMS, Compose
+
+
+@DATASETS.register_module()
+class ScanNetDataset(Dataset):
+    class2id = np.array(VALID_CLASS_IDS_20)
+
+    def __init__(
+        self,
+        split="train",
+        data_root="data/scannet",
+        transform=None,
+        lr_file=None,
+        la_file=None,
+        ignore_index=-1,
+        test_mode=False,
+        test_cfg=None,
+        cache=False,
+        loop=1,
+    ):
+        super(ScanNetDataset, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.cache = cache
+        self.loop = loop if not test_mode else 1  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+
+        if test_mode:
+            self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+            self.test_crop = TRANSFORMS.build(
+                self.test_cfg.crop) if self.test_cfg.crop else None
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [
+                Compose(aug) for aug in self.test_cfg.aug_transform
+            ]
+
+        if lr_file:
+            self.data_list = [
+                os.path.join(data_root, "train", name + ".pth")
+                for name in np.loadtxt(lr_file, dtype=str)
+            ]
+        else:
+            self.data_list = self.get_data_list()
+        self.la = torch.load(la_file) if la_file else None
+        self.ignore_index = ignore_index
+        logger = get_root_logger()
+        logger.info("Totally {} x {} samples in {} set.".format(
+            len(self.data_list), self.loop, split))
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(
+                os.path.join(self.data_root, self.split, "*.pth"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(
+                    os.path.join(self.data_root, split, "*.pth"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+        if not self.cache:
+            data = torch.load(data_path)
+        else:
+            data_name = data_path.replace(os.path.dirname(self.data_root),
+                                          "").split(".")[0]
+            cache_name = "pointcept" + data_name.replace(os.path.sep, "-")
+            data = shared_dict(cache_name)
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        scene_id = data["scene_id"]
+        if "semantic_gt20" in data.keys():
+            segment = data["semantic_gt20"].reshape([-1])
+        else:
+            segment = np.ones(coord.shape[0]) * -1
+        if "instance_gt" in data.keys():
+            instance = data["instance_gt"].reshape([-1])
+        else:
+            instance = np.ones(coord.shape[0]) * -1
+        data_dict = dict(
+            coord=coord,
+            normal=normal,
+            color=color,
+            segment=segment,
+            instance=instance,
+            scene_id=scene_id,
+        )
+        if self.la:
+            sampled_index = self.la[self.get_data_name(idx)]
+            mask = np.ones_like(segment).astype(np.bool)
+            mask[sampled_index] = False
+            segment[mask] = self.ignore_index
+            data_dict["segment"] = segment
+            data_dict["sampled_index"] = sampled_index
+        return data_dict
+
+    def get_data_name(self, idx):
+        return os.path.basename(
+            self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        segment = data_dict.pop("segment")
+        data_dict = self.transform(data_dict)
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        input_dict_list = []
+        for data in data_dict_list:
+            data_part_list = self.test_voxelize(data)
+            for data_part in data_part_list:
+                if self.test_crop:
+                    data_part = self.test_crop(data_part)
+                else:
+                    data_part = [data_part]
+                input_dict_list += data_part
+
+        for i in range(len(input_dict_list)):
+            input_dict_list[i] = self.post_transform(input_dict_list[i])
+        data_dict = dict(fragment_list=input_dict_list,
+                         segment=segment,
+                         name=self.get_data_name(idx))
+        return data_dict
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ScanNet200Dataset(ScanNetDataset):
+    class2id = np.array(VALID_CLASS_IDS_200)
+
+    def get_data(self, idx):
+        data = torch.load(self.data_list[idx % len(self.data_list)])
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        scene_id = data["scene_id"]
+        if "semantic_gt200" in data.keys():
+            segment = data["semantic_gt200"].reshape([-1])
+        else:
+            segment = np.ones(coord.shape[0]) * -1
+        if "instance_gt" in data.keys():
+            instance = data["instance_gt"].reshape([-1])
+        else:
+            instance = np.ones(coord.shape[0]) * -1
+        data_dict = dict(
+            coord=coord,
+            normal=normal,
+            color=color,
+            segment=segment,
+            instance=instance,
+            scene_id=scene_id,
+        )
+        if self.la:
+            sampled_index = self.la[self.get_data_name(idx)]
+            segment[sampled_index] = self.ignore_index
+            data_dict["segment"] = segment
+            data_dict["sampled_index"] = sampled_index
+        return data_dict
+
+
+@DATASETS.register_module()
+class ScanNetDatasetV2(DefaultDatasetV2):
+    VALID_ASSETS = [
+        "coord",
+        "color",
+        "normal",
+        "segment20",
+        "instance",
+    ]
+    class2id = np.array(VALID_CLASS_IDS_20)
+
+    def __init__(
+        self,
+        lr_file=None,
+        la_file=None,
+        **kwargs,
+    ):
+        self.lr = np.loadtxt(lr_file,
+                             dtype=str) if lr_file is not None else None
+        self.la = torch.load(la_file) if la_file is not None else None
+        super().__init__(**kwargs)
+
+    def get_data_list(self):
+        if self.lr is None:
+            data_list = super().get_data_list()
+        else:
+            data_list = [
+                os.path.join(self.data_root, "train", name) for name in self.lr
+            ]
+        return data_list
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+        name = self.get_data_name(idx)
+        if self.cache:
+            cache_name = f"pointcept-{name}"
+            return shared_dict(cache_name)
+
+        data_dict = {}
+        assets = os.listdir(data_path)
+        for asset in assets:
+            if not asset.endswith(".npy"):
+                continue
+            if asset[:-4] not in self.VALID_ASSETS:
+                continue
+            data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+        data_dict["name"] = name
+        data_dict["coord"] = data_dict["coord"].astype(np.float32)
+        data_dict["color"] = data_dict["color"].astype(np.float32)
+        data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+        if "segment20" in data_dict.keys():
+            data_dict["segment"] = (data_dict.pop("segment20").reshape(
+                [-1]).astype(np.int32))
+        elif "segment200" in data_dict.keys():
+            data_dict["segment"] = (data_dict.pop("segment200").reshape(
+                [-1]).astype(np.int32))
+        else:
+            data_dict["segment"] = (
+                np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1)
+
+        if "instance" in data_dict.keys():
+            data_dict["instance"] = (data_dict.pop("instance").reshape(
+                [-1]).astype(np.int32))
+        else:
+            data_dict["instance"] = (
+                np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1)
+        if self.la:
+            sampled_index = self.la[self.get_data_name(idx)]
+            mask = np.ones_like(data_dict["segment"], dtype=bool)
+            mask[sampled_index] = False
+            data_dict["segment"][mask] = self.ignore_index
+            data_dict["sampled_index"] = sampled_index
+        return data_dict
+
+
+@DATASETS.register_module()
+class ScanNet200DatasetV2(ScanNetDatasetV2):
+    VALID_ASSETS = [
+        "coord",
+        "color",
+        "normal",
+        "segment200",
+        "instance",
+    ]
+    class2id = np.array(VALID_CLASS_IDS_200)

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannet_pair.py

@@ -0,0 +1,89 @@
+"""
+ScanNet Pair Dataset (Frame-level contrastive view)
+
+Refer PointContrast
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+
+
+@DATASETS.register_module()
+class ScanNetPairDataset(Dataset):
+    def __init__(
+        self,
+        data_root="data/scannet_pair",
+        overlap_threshold=0.3,
+        view1_transform=None,
+        view2_transform=None,
+        loop=1,
+        **kwargs
+    ):
+        super(ScanNetPairDataset, self).__init__()
+        self.data_root = data_root
+        self.overlap_threshold = overlap_threshold
+        self.view1_transform = Compose(view1_transform)
+        self.view2_transform = Compose(view2_transform)
+        self.loop = loop
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info("Totally {} x {} samples.".format(len(self.data_list), self.loop))
+
+    def get_data_list(self):
+        data_list = []
+        overlap_list = glob.glob(
+            os.path.join(self.data_root, "*", "pcd", "overlap.txt")
+        )
+        for overlap_file in overlap_list:
+            with open(overlap_file) as f:
+                overlap = f.readlines()
+            overlap = [pair.strip().split() for pair in overlap]
+            data_list.extend(
+                [
+                    pair[:2]
+                    for pair in overlap
+                    if float(pair[2]) > self.overlap_threshold
+                ]
+            )
+        return data_list
+
+    def get_data(self, idx):
+        pair = self.data_list[idx % len(self.data_list)]
+        view1_dict = torch.load(self.data_root + pair[0])
+        view2_dict = torch.load(self.data_root + pair[1])
+        return view1_dict, view2_dict
+
+    def get_data_name(self, idx):
+        return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        view1_dict, view2_dict = self.get_data(idx)
+        view1_dict = self.view1_transform(view1_dict)
+        view2_dict = self.view2_transform(view2_dict)
+        data_dict = dict()
+        for key, value in view1_dict.items():
+            data_dict["view1_" + key] = value
+        for key, value in view2_dict.items():
+            data_dict["view2_" + key] = value
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        raise NotImplementedError
+
+    def __getitem__(self, idx):
+        return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/scannetpp.py

@@ -0,0 +1,78 @@
+"""
+ScanNet++ dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+import glob
+
+from pointcept.utils.cache import shared_dict
+
+from .builder import DATASETS
+from .defaults import DefaultDatasetV2
+
+
+@DATASETS.register_module()
+class ScanNetPPDataset(DefaultDatasetV2):
+    VALID_ASSETS = [
+        "coord",
+        "color",
+        "normal",
+        "segment",
+        "instance",
+    ]
+
+    def __init__(
+        self,
+        multilabel=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.multilabel = multilabel
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+        name = self.get_data_name(idx)
+        if self.cache:
+            cache_name = f"pointcept-{name}"
+            return shared_dict(cache_name)
+
+        data_dict = {}
+        assets = os.listdir(data_path)
+        for asset in assets:
+            if not asset.endswith(".npy"):
+                continue
+            if asset[:-4] not in self.VALID_ASSETS:
+                continue
+            data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
+        data_dict["name"] = name
+
+        if "coord" in data_dict.keys():
+            data_dict["coord"] = data_dict["coord"].astype(np.float32)
+
+        if "color" in data_dict.keys():
+            data_dict["color"] = data_dict["color"].astype(np.float32)
+
+        if "normal" in data_dict.keys():
+            data_dict["normal"] = data_dict["normal"].astype(np.float32)
+
+        if not self.multilabel:
+            if "segment" in data_dict.keys():
+                data_dict["segment"] = data_dict["segment"][:, 0].astype(np.int32)
+            else:
+                data_dict["segment"] = (
+                    np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+                )
+
+            if "instance" in data_dict.keys():
+                data_dict["instance"] = data_dict["instance"][:, 0].astype(np.int32)
+            else:
+                data_dict["instance"] = (
+                    np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
+                )
+        else:
+            raise NotImplementedError
+        return data_dict

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/semantic_kitti.py

@@ -0,0 +1,139 @@
+"""
+Semantic KITTI dataset
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class SemanticKITTIDataset(DefaultDataset):
+    def __init__(self, ignore_index=-1, **kwargs):
+        self.ignore_index = ignore_index
+        self.learning_map = self.get_learning_map(ignore_index)
+        self.learning_map_inv = self.get_learning_map_inv(ignore_index)
+        super().__init__(ignore_index=ignore_index, **kwargs)
+
+    def get_data_list(self):
+        split2seq = dict(
+            train=[0, 1, 2, 3, 4, 5, 6, 7, 9, 10],
+            val=[8],
+            test=[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
+        )
+        if isinstance(self.split, str):
+            seq_list = split2seq[self.split]
+        elif isinstance(self.split, list):
+            seq_list = []
+            for split in self.split:
+                seq_list += split2seq[split]
+        else:
+            raise NotImplementedError
+
+        data_list = []
+        for seq in seq_list:
+            seq = str(seq).zfill(2)
+            seq_folder = os.path.join(self.data_root, "dataset", "sequences", seq)
+            seq_files = sorted(os.listdir(os.path.join(seq_folder, "velodyne")))
+            data_list += [
+                os.path.join(seq_folder, "velodyne", file) for file in seq_files
+            ]
+        return data_list
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+        with open(data_path, "rb") as b:
+            scan = np.fromfile(b, dtype=np.float32).reshape(-1, 4)
+        coord = scan[:, :3]
+        strength = scan[:, -1].reshape([-1, 1])
+
+        label_file = data_path.replace("velodyne", "labels").replace(".bin", ".label")
+        if os.path.exists(label_file):
+            with open(label_file, "rb") as a:
+                segment = np.fromfile(a, dtype=np.int32).reshape(-1)
+                segment = np.vectorize(self.learning_map.__getitem__)(
+                    segment & 0xFFFF
+                ).astype(np.int32)
+        else:
+            segment = np.zeros(scan.shape[0]).astype(np.int32)
+        data_dict = dict(coord=coord, strength=strength, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        file_path = self.data_list[idx % len(self.data_list)]
+        dir_path, file_name = os.path.split(file_path)
+        sequence_name = os.path.basename(os.path.dirname(dir_path))
+        frame_name = os.path.splitext(file_name)[0]
+        data_name = f"{sequence_name}_{frame_name}"
+        return data_name
+
+    @staticmethod
+    def get_learning_map(ignore_index):
+        learning_map = {
+            0: ignore_index,  # "unlabeled"
+            1: ignore_index,  # "outlier" mapped to "unlabeled" --------------------------mapped
+            10: 0,  # "car"
+            11: 1,  # "bicycle"
+            13: 4,  # "bus" mapped to "other-vehicle" --------------------------mapped
+            15: 2,  # "motorcycle"
+            16: 4,  # "on-rails" mapped to "other-vehicle" ---------------------mapped
+            18: 3,  # "truck"
+            20: 4,  # "other-vehicle"
+            30: 5,  # "person"
+            31: 6,  # "bicyclist"
+            32: 7,  # "motorcyclist"
+            40: 8,  # "road"
+            44: 9,  # "parking"
+            48: 10,  # "sidewalk"
+            49: 11,  # "other-ground"
+            50: 12,  # "building"
+            51: 13,  # "fence"
+            52: ignore_index,  # "other-structure" mapped to "unlabeled" ------------------mapped
+            60: 8,  # "lane-marking" to "road" ---------------------------------mapped
+            70: 14,  # "vegetation"
+            71: 15,  # "trunk"
+            72: 16,  # "terrain"
+            80: 17,  # "pole"
+            81: 18,  # "traffic-sign"
+            99: ignore_index,  # "other-object" to "unlabeled" ----------------------------mapped
+            252: 0,  # "moving-car" to "car" ------------------------------------mapped
+            253: 6,  # "moving-bicyclist" to "bicyclist" ------------------------mapped
+            254: 5,  # "moving-person" to "person" ------------------------------mapped
+            255: 7,  # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+            256: 4,  # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+            257: 4,  # "moving-bus" mapped to "other-vehicle" -------------------mapped
+            258: 3,  # "moving-truck" to "truck" --------------------------------mapped
+            259: 4,  # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+        }
+        return learning_map
+
+    @staticmethod
+    def get_learning_map_inv(ignore_index):
+        learning_map_inv = {
+            ignore_index: ignore_index,  # "unlabeled"
+            0: 10,  # "car"
+            1: 11,  # "bicycle"
+            2: 15,  # "motorcycle"
+            3: 18,  # "truck"
+            4: 20,  # "other-vehicle"
+            5: 30,  # "person"
+            6: 31,  # "bicyclist"
+            7: 32,  # "motorcyclist"
+            8: 40,  # "road"
+            9: 44,  # "parking"
+            10: 48,  # "sidewalk"
+            11: 49,  # "other-ground"
+            12: 50,  # "building"
+            13: 51,  # "fence"
+            14: 70,  # "vegetation"
+            15: 71,  # "trunk"
+            16: 72,  # "terrain"
+            17: 80,  # "pole"
+            18: 81,  # "traffic-sign"
+        }
+        return learning_map_inv

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/shapenet_part.py

@@ -0,0 +1,160 @@
+"""
+ShapeNet Part Dataset (Unmaintained)
+
+get processed shapenet part dataset
+at "https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip"
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import json
+import torch
+import numpy as np
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ShapeNetPartDataset(Dataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/shapenetcore_partanno_segmentation_benchmark_v0_normal",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+    ):
+        super(ShapeNetPartDataset, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.loop = (
+            loop if not test_mode else 1
+        )  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+        self.cache = {}
+
+        # load categories file
+        self.categories = []
+        self.category2part = {
+            "Airplane": [0, 1, 2, 3],
+            "Bag": [4, 5],
+            "Cap": [6, 7],
+            "Car": [8, 9, 10, 11],
+            "Chair": [12, 13, 14, 15],
+            "Earphone": [16, 17, 18],
+            "Guitar": [19, 20, 21],
+            "Knife": [22, 23],
+            "Lamp": [24, 25, 26, 27],
+            "Laptop": [28, 29],
+            "Motorbike": [30, 31, 32, 33, 34, 35],
+            "Mug": [36, 37],
+            "Pistol": [38, 39, 40],
+            "Rocket": [41, 42, 43],
+            "Skateboard": [44, 45, 46],
+            "Table": [47, 48, 49],
+        }
+        self.token2category = {}
+        with open(os.path.join(self.data_root, "synsetoffset2category.txt"), "r") as f:
+            for line in f:
+                ls = line.strip().split()
+                self.token2category[ls[1]] = len(self.categories)
+                self.categories.append(ls[0])
+
+        if test_mode:
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        # load data list
+        if isinstance(self.split, str):
+            self.data_list = self.load_data_list(self.split)
+        elif isinstance(self.split, list):
+            self.data_list = []
+            for s in self.split:
+                self.data_list += self.load_data_list(s)
+        else:
+            raise NotImplementedError
+
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_idx), self.loop, split
+            )
+        )
+
+    def load_data_list(self, split):
+        split_file = os.path.join(
+            self.data_root,
+            "train_test_split",
+            "shuffled_{}_file_list.json".format(split),
+        )
+        if not os.path.isfile(split_file):
+            raise (RuntimeError("Split file do not exist: " + split_file + "\n"))
+        with open(split_file, "r") as f:
+            # drop "shape_data/" and append ".txt"
+            data_list = [
+                os.path.join(self.data_root, data[11:] + ".txt")
+                for data in json.load(f)
+            ]
+        return data_list
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_idx = idx % len(self.data_list)
+        if data_idx in self.cache:
+            coord, norm, segment, cls_token = self.cache[data_idx]
+        else:
+            data = np.loadtxt(self.data_list[data_idx]).astype(np.float32)
+            cls_token = self.token2category[
+                os.path.basename(os.path.dirname(self.data_list[data_idx]))
+            ]
+            coord, norm, segment = (
+                data[:, :3],
+                data[:, 3:6],
+                data[:, 6].astype(np.int32),
+            )
+            self.cache[data_idx] = (coord, norm, segment, cls_token)
+
+        data_dict = dict(coord=coord, norm=norm, segment=segment, cls_token=cls_token)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_idx = self.data_idx[idx % len(self.data_idx)]
+        data = np.loadtxt(self.data_list[data_idx]).astype(np.float32)
+        cls_token = self.token2category[
+            os.path.basename(os.path.dirname(self.data_list[data_idx]))
+        ]
+        coord, norm, segment = data[:, :3], data[:, 3:6], data[:, 6].astype(np.int32)
+
+        data_dict = dict(coord=coord, norm=norm, cls_token=cls_token)
+        data_dict = self.transform(data_dict)
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(self.post_transform(aug(deepcopy(data_dict))))
+        data_dict = dict(
+            fragment_list=data_dict_list, segment=segment, name=self.get_data_name(idx)
+        )
+        return data_dict
+
+    def get_data_name(self, idx):
+        data_idx = self.data_idx[idx % len(self.data_idx)]
+        return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_idx) * self.loop

scannet/insseg-pointgroup-v1m1-pt-v3m1-ppt-ft/code/pointcept/datasets/structure3d.py

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+"""
+Structured3D Datasets
+
+Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
+Please cite our work if the code is helpful to you.
+"""
+
+import glob
+import os
+from collections.abc import Sequence
+
+from .builder import DATASETS
+from .defaults import DefaultDataset, DefaultDatasetV2
+
+
+@DATASETS.register_module()
+class Structured3DDataset(DefaultDataset):
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(
+                os.path.join(self.data_root, self.split, "scene_*/room_*"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(
+                    os.path.join(self.data_root, split, "scene_*/room_*"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data_name(self, idx):
+        file_path = self.data_list[idx % len(self.data_list)]
+        dir_path, room_name = os.path.split(file_path)
+        scene_name = os.path.basename(dir_path)
+        data_name = f"{scene_name}_{room_name}"
+        return data_name
+
+
+@DATASETS.register_module()
+class Structured3DDatasetV2(DefaultDatasetV2):
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(
+                os.path.join(self.data_root, self.split, "scene_*/room_*"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(
+                    os.path.join(self.data_root, split, "scene_*/room_*"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data_name(self, idx):
+        file_path = self.data_list[idx % len(self.data_list)]
+        dir_path, room_name = os.path.split(file_path)
+        scene_name = os.path.basename(dir_path)
+        data_name = f"{scene_name}_{room_name}"
+        return data_name