https://github.com/POSTECH-CVLab/point-transformer
This is an unofficial implementation of the Point Transformer paper.
https://github.com/POSTECH-CVLab/point-transformer
Last synced: about 1 year ago
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This is an unofficial implementation of the Point Transformer paper.
- Host: GitHub
- URL: https://github.com/POSTECH-CVLab/point-transformer
- Owner: POSTECH-CVLab
- Created: 2021-01-07T07:37:31.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-04-19T15:41:29.000Z (almost 4 years ago)
- Last Synced: 2025-03-09T04:36:46.173Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 3.37 MB
- Stars: 548
- Watchers: 6
- Forks: 103
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
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README
# Point Transformer
This repository reproduces [Point Transformer](https://arxiv.org/abs/2012.09164). \
The codebase is provided by the first author of [Point Transformer](https://arxiv.org/abs/2012.09164).
## 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](https://arxiv.org/abs/2012.09164)
- To create conda environment, command as follows:
```
bash env_setup.sh pt
```
## Dataset preparation
- Download S3DIS [dataset](https://drive.google.com/uc?export=download&id=1KUxWagmEWnvMhEb4FRwq2Mj0aa3U3xUf) 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](https://arxiv.org/abs/2012.09164):
```
@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](https://arxiv.org/abs/2012.09164).
We also refer [PAConv repository](https://github.com/CVMI-Lab/PAConv).