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naderalfares/ModelNet40_Auto_aligned

ModelNet40 Auto Aligned Auto-aligned version of the ModelNet40 3D CAD dataset. Each sample is an OFF mesh file organized by class and train/test split. This dataset mirrors the layout of naderalfares/ModelNet40, but uses the auto-aligned meshes from the Princeton ModelNet release. Dataset structure modelnet40_auto_aligned/ {class}/ train/{class}_{id}.off test/{class}_{id}.off 40 classes (airplane, bathtub, bed, …, xbox) 9,843 training meshes 2,468 test… See the full description on the dataset page: https://huggingface.co/datasets/naderalfares/ModelNet40_Auto_aligned.

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Dataset Card

ModelNet40 Auto Aligned

Auto-aligned version of the ModelNet40 3D CAD dataset. Each sample is an OFF mesh file organized by class and train/test split.

This dataset mirrors the layout of `naderalfares/ModelNet40`, but uses the auto-aligned meshes from the Princeton ModelNet release.

Dataset structure

modelnet40_auto_aligned/
  {class}/
    train/{class}_{id}.off
    test/{class}_{id}.off
  • —40 classes (airplane, bathtub, bed, …, xbox)
  • —9,843 training meshes
  • —2,468 test meshes
  • —12,311 meshes total (~9.7 GB)

The parquet manifest stores metadata only. Mesh files live under modelnet40_auto_aligned/ and are referenced by the object_path column (without that prefix).

Load metadata with 🤗 Datasets

python
from datasets import load_dataset

ds = load_dataset("naderalfares/ModelNet40_Auto_aligned")

print(ds)
# DatasetDict({
#     train: Dataset({ features: ['object_id', 'class', 'split', 'object_path', '__index_level_0__'], num_rows: 9843 })
#     test:  Dataset({ features: ['object_id', 'class', 'split', 'object_path', '__index_level_0__'], num_rows: 2468 })
# })

row = ds["train"][0]
print(row)
# {'object_id': 'airplane_0001', 'class': 'airplane', 'split': 'train',
#  'object_path': 'airplane/train/airplane_0001.off', '__index_level_0__': 100}

Download a mesh file

python
from huggingface_hub import hf_hub_download

repo_id = "naderalfares/ModelNet40_Auto_aligned"
row = ds["train"][0]

mesh_path = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename=f"modelnet40_auto_aligned/{row['object_path']}",
)
print(mesh_path)  # local path to airplane_0001.off

Citation

If you use this dataset, please cite the original ModelNet paper and the auto-alignment work:

bibtex
@inproceedings{wu20153d,
  title={3D ShapeNets: A Deep Representation for Volumetric Shapes},
  author={Wu, Zhirong and Song, Shuran and Khademi, Adarsh and Zhao, Tian and others},
  booktitle={CVPR},
  year={2015}
}

License

MIT

naderalfares/ModelNet40_Auto_aligned · CoolFace