Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021)
This is the implementation of PSD (ICCV 2021), a simple weakly-supervised semantic segmentation of large-scale 3D point clouds.
This code has been tested with Python 3.5, Tensorflow 1.13, CUDA 9.0 and cuDNN 7.4.1 on Ubuntu 16.04.
- Clone the repository
git clone --depth=1 https://github.com/Yachao-Zhang/PSD && cd PSD
- Setup python environment
pip install -r helper_requirements.txt
sh compile_op.sh
S3DIS dataset can be found
here.
Download the files named "Stanford3dDataset_v1.2_Aligned_Version.zip". Uncompress the folder and move it to
/data/S3DIS.
- Preparing the dataset:
python utils/data_prepare_s3dis.py
Training and test of weakly semantic Segmentation on S3DIS Area-5 by:
sh jobs_s3dis_a5.sh
If you find our work useful in your research, please consider citing:
@inproceedings{zhang2021perturbed,
title={Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation},
author={Zhang, Yachao and Qu, Yanyun and Xie, Yuan and Li, Zonghao and Zheng, Shanshan and Li, Cuihua},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={15520--15528},
year={2021}
}
A related work (Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud AAAI-2021) can be found here.
@inproceedings{zhang2021weakly,
title={Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud},
author={Zhang, Yachao and Li, Zonghao and Xie, Yuan and Qu, Yanyun and Li, Cuihua and Mei, Tao},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={4},
pages={3421--3429},
year={2021}
}
Note that this code is heavily borrowed from RandLA-Net (https://github.com/QingyongHu/RandLA-Net).
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