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@@ -58,7 +58,7 @@ Less is more. We proved that, with only the high-quality objects in a 3D dataset
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  To address the prevalence of low-quality models in Objaverse, we:
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  1. Manually annotated 10,000 3D objects with quality and characteristic attributes;
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  2. Trained a neural network capable of annotating tags for the rest of the Objaverse dataset;
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- 3. Created a curated subset of approximately 500,000 high-quality 3D models.
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  Our experiments show that:
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  - Models trained on our quality-focused subset achieve better performance than those trained on the larger Objaverse dataset in image-to-3D generation tasks;
 
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  To address the prevalence of low-quality models in Objaverse, we:
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  1. Manually annotated 10,000 3D objects with quality and characteristic attributes;
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  2. Trained a neural network capable of annotating tags for the rest of the Objaverse dataset;
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+ 3. Annotated the Objaverse (~790k data) with quality and characteristic tags.
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  Our experiments show that:
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  - Models trained on our quality-focused subset achieve better performance than those trained on the larger Objaverse dataset in image-to-3D generation tasks;