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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"from tqdm.auto import tqdm\n",
"\n",
"from point_e.models.download import load_checkpoint\n",
"from point_e.models.configs import MODEL_CONFIGS, model_from_config\n",
"from point_e.util.pc_to_mesh import marching_cubes_mesh\n",
"from point_e.util.plotting import plot_point_cloud\n",
"from point_e.util.point_cloud import PointCloud"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"print('creating SDF model...')\n",
"name = 'sdf'\n",
"model = model_from_config(MODEL_CONFIGS[name], device)\n",
"model.eval()\n",
"\n",
"print('loading SDF model...')\n",
"model.load_state_dict(load_checkpoint(name, device))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load a point cloud we want to convert into a mesh.\n",
"pc = PointCloud.load('example_data/pc_corgi.npz')\n",
"\n",
"# Plot the point cloud as a sanity check.\n",
"fig = plot_point_cloud(pc, grid_size=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Produce a mesh (with vertex colors)\n",
"mesh = marching_cubes_mesh(\n",
" pc=pc,\n",
" model=model,\n",
" batch_size=4096,\n",
" grid_size=32, # increase to 128 for resolution used in evals\n",
" progress=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Write the mesh to a PLY file to import into some other program.\n",
"with open('mesh.ply', 'wb') as f:\n",
" mesh.write_ply(f)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"vscode": {
"interpreter": {
"hash": "b270b0f43bc427bcab7703c037711644cc480aac7c1cc8d2940cfaf0b447ee2e"
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"nbformat": 4,
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