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PART: Learning 3D Part Assembly and Retrieval with Transformers
Dataset Details
custom (87,224 samples total)
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├── 📋 Source dataset metadata (JSON)
│ ├── Compat.json 8,453 entries {model_id, gltf_file, num_parts, part_names}
│ ├── PartNext.json 20,726 entries {object_id, num_parts}
│ └── PartverseXL.json 36,039 entries {shape_id, num_parts, part_indices}
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├── 📦 Per-sample data (87,224 .npy files each, indexed by sample ID)
│ ├── shape_data/ dict: {part_pcs (P,1000,3), part_poses (P,7), part_ids (P,), geo_part_ids (P,), sym (P,3)} part point clouds info
│ ├── contact_points/ array (P, P, 4) pairwise contact info
│ └── model_pc_labeled/ dict: {points (10000,3), instance (10000,)} target point cloud info
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└── 📑 Split files
├── train.npy 78,501 sample IDs
└── test.npy 8,723 sample IDs
Dataset Sources
This dataset is built upon the following datasets. We sincerely thank the authors and contributors for their work:
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