point-mae-base.pretrain.yatian-pang

Model card for point-mae-base.pretrain.yatian-pang

A Point-MAE self-supervised pretraining model (masked point autoencoder). Pretrained on ShapeNet-55.

Model Details

• Model Type: Self-supervised pretraining
• Model Stats:
  • Params (M): 29.0
• Dataset: ShapeNet-55
• Paper: Masked Autoencoders for Point Cloud Self-supervised Learning
• Converted from: Pang-Yatian/Point-MAE (MIT)
• Library: torch-pointcloud

Install

pip install torch-pointcloud

Usage

import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate

model, info = tp.create_model(
    "point-mae-base.pretrain.yatian-pang",
    pretrained=True,
    return_info=True,
)
model = model.eval()

# synthetic sample with the keys a dataset provides
num_points = 8192
sample = {
    "pos": torch.randn(num_points, 3),
}
data = collate([sample])

with torch.no_grad():
    out = model(data.get("x"), data["pos"], data["batch"])

Citation

@inproceedings{pang2022pointmae,
  title   = {Masked Autoencoders for Point Cloud Self-supervised Learning},
  author  = {Yatian Pang and Wenxiao Wang and Francis E. H. Tay and Wei Liu and Yonghong Tian and Li Yuan},
  booktitle = {ECCV},
  year    = {2022}
}

@article{chang2015shapenet,
  author  = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong and Yi, Li and Yu, Fisher},
  title   = {{ShapeNet}: An Information-Rich {3D} Model Repository},
  journal = {arXiv preprint arXiv:1512.03012},
  year    = {2015},
}

@software{dujardin2026pytorchpointcloud,
  author  = {Arthur Dujardin},
  title   = {PyTorch PointCloud},
  year    = {2026},
  doi     = {10.5281/zenodo.22159632},
  url     = {https://github.com/arthurdjn/pytorch-pointcloud},
}