Point Transformer

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

Forked Additions

This repository remains largely unchanged with the exception of the training configurations needed to adapt the Point Transformer model to my experiments.

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

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.

LucHayward/point-transformer