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.