pointgpt-l.pretrain.guangyan-chen

Model card for pointgpt-l.pretrain.guangyan-chen

A PointGPT self-supervised pretraining model (autoregressive generative pretraining transformer). Pretrained on ShapeNet-55.

Model Details

• Model Type: Self-supervised pretraining
• Model Stats:
  • Params (M): 359.8
• Dataset: ShapeNet-55
• Paper: PointGPT: Auto-regressively Generative Pre-training from Point Clouds
• Converted from: CGuangyan-BIT/PointGPT (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(
    "pointgpt-l.pretrain.guangyan-chen",
    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{chen2023pointgpt,
  title   = {PointGPT: Auto-regressively Generative Pre-training from Point Clouds},
  author  = {Guangyan Chen and Meiling Wang and Yi Yang and Kai Yu and Li Yuan and Yufeng Yue},
  booktitle = {NeurIPS},
  year    = {2023}
}

@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},
}