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.

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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:

  sh 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
      

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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

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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.