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  1. comp/meshgpt/inf_mesh_topp.tar.gz +3 -0
  2. comp/meshxl/outputs-bench.tar.gz +3 -0
  3. comp/meshxl/sampled_table.tar.gz +3 -0
  4. objaverse/objaverse_occ_v5_ids.tar.gz/objaverse_occ_v5_ids.tar.gz +3 -0
  5. objaverse/split/test.npz +3 -0
  6. objaverse/split/train.npz +3 -0
  7. pretrained/bench-270k/model.safetensors +3 -0
  8. pretrained/bench-340k/model.safetensors +3 -0
  9. pretrained/bench-410k/model.safetensors +3 -0
  10. pretrained/bench-446k/model.safetensors +3 -0
  11. pretrained/chair-330k/model.safetensors +3 -0
  12. pretrained/chair-469k/model.safetensors +3 -0
  13. pretrained/chair-490k/model.safetensors +3 -0
  14. pretrained/inference-table-bench/render_dump.tar.gz +3 -0
  15. pretrained/inference/render_dump_chair.tar.gz +3 -0
  16. pretrained/inference/render_dump_lamp.tar.gz +3 -0
  17. pretrained/lamp-210k/model.safetensors +3 -0
  18. pretrained/lamp-346k/model.safetensors +3 -0
  19. pretrained/obj/model.safetensors +3 -0
  20. pretrained/obj_v1/model.safetensors +3 -0
  21. pretrained/table-300k/model.safetensors +3 -0
  22. pretrained/table-420k/model.safetensors +3 -0
  23. pretrained/table-437k/model.safetensors +3 -0
  24. pretrained/table-490k/model.safetensors +3 -0
  25. shapenet/02828884_train_decimated.npz +3 -0
  26. shapenet/03001627_train_decimated.npz +3 -0
  27. shapenet/03636649_train_decimated.npz +3 -0
  28. shapenet/04379243_train_decimated.npz +3 -0
  29. shapenet/bench_mean_std.npz +3 -0
  30. shapenet/chair_mean_std.npz +3 -0
  31. shapenet/lamp_mean_std.npz +3 -0
  32. shapenet/shapenet-split.tar.gz +3 -0
  33. shapenet/table_mean_std.npz +3 -0
  34. shapenet_synset_dict_v2.json +57 -0
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+ {
2
+ "02691156": "airplane",
3
+ "02747177": "trash bin",
4
+ "02773838": "bag",
5
+ "02801938": "basket",
6
+ "02808440": "bathtub",
7
+ "02818832": "bed",
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+ "02828884": "bench",
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+ "02843684": "birdhouse",
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+ "02871439": "bookshelf",
11
+ "02876657": "bottle",
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+ "02880940": "bowl",
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+ "02924116": "bus",
14
+ "02933112": "cabinet",
15
+ "02942699": "camera",
16
+ "02946921": "can",
17
+ "02954340": "cap",
18
+ "02958343": "car",
19
+ "02992529": "cellphone",
20
+ "03001627": "chair",
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+ "03046257": "clock",
22
+ "03085013": "keyboard",
23
+ "03207941": "dishwasher",
24
+ "03211117": "display",
25
+ "03261776": "earphone",
26
+ "03325088": "faucet",
27
+ "03337140": "file cabinet",
28
+ "03467517": "guitar",
29
+ "03513137": "helmet",
30
+ "03593526": "jar",
31
+ "03624134": "knife",
32
+ "03636649": "lamp",
33
+ "03642806": "laptop",
34
+ "03691459": "loudspeaker",
35
+ "03710193": "mailbox",
36
+ "03759954": "microphone",
37
+ "03761084": "microwaves",
38
+ "03790512": "motorbike",
39
+ "03797390": "mug",
40
+ "03928116": "piano",
41
+ "03938244": "pillow",
42
+ "03948459": "pistol",
43
+ "03991062": "flowerpot",
44
+ "04004475": "printer",
45
+ "04074963": "remote",
46
+ "04090263": "rifle",
47
+ "04099429": "rocket",
48
+ "04225987": "skateboard",
49
+ "04256520": "sofa",
50
+ "04330267": "stove",
51
+ "04379243": "table",
52
+ "04401088": "telephone",
53
+ "04460130": "tower",
54
+ "04468005": "train",
55
+ "04530566": "watercraft",
56
+ "04554684": "washer"
57
+ }