Skip to content

Latest commit

 

History

History
74 lines (59 loc) · 2.16 KB

README.md

File metadata and controls

74 lines (59 loc) · 2.16 KB
Raw

Point Transformer

This repository reproduces Point Transformer.
The codebase is provided by the first author of Point Transformer.

Notes

  • For shape classification and part segmentation, please use paconv-codebase branch. After some testing, we will merge it into the master branch.

Dependencies

  • Ubuntu: 18.04 or higher

  • PyTorch: 1.9.0

  • CUDA: 11.1

  • Hardware: 4GPUs (TITAN RTX) to reproduce Point Transformer

  • To create conda environment, command as follows:

    bash env_setup.sh pt

Dataset preparation

  • Download S3DIS dataset and symlink the paths to them as follows:

    mkdir -p dataset
ln -s /path_to_s3dis_dataset dataset/s3dis

Usage

  • Shape classification on ModelNet40
    • For now, please use paconv-codebase branch.
  • Part segmentation on ShapeNetPart
    • For now, please use paconv-codebase branch.
  • Semantic segmantation on S3DIS Area 5

    • Train

      • Specify the gpu used in config and then do training:

        sh tool/train.sh s3dis pointtransformer_repro

    • Test

      • Afer training, you can test the checkpoint as follows:

        CUDA_VISIBLE_DEVICES=0 sh tool/test.sh s3dis pointtransformer_repro

Experimental Results

  • Semanctic Segmentation on S3DIS Area 5

    Model mAcc OA mIoU
    Paper 76.5 90.8 70.4
    Hengshuang's code 76.8 90.4 70.0

References

If you use this code, please cite Point Transformer:

@inproceedings{zhao2021point,
  title={Point transformer},
  author={Zhao, Hengshuang and Jiang, Li and Jia, Jiaya and Torr, Philip HS and Koltun, Vladlen},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={16259--16268},
  year={2021}
}

Acknowledgement

The code is from the first author of Point Transformer. We also refer PAConv repository.