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PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

This repository contains a PyTorch implementation of the paper:

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification. <br> Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu. <br> CVPR 2020 (Oral)

Dependencies

• Python 3.6
• CUDA 10.0.
• PyTorch. Codes are tested with version 1.2.0
• (Optional) TensorboardX for visualization of the training process.

Following is the suggested way to install these dependencies:

# Create a new conda environment
conda create -n PointAugment python=3.6
conda activate PointAugment

# Install pytorch (please refer to the commend in the official website)
conda install pytorch=1.2.0 torchvision cudatoolkit=10.0 -c pytorch -y

Usage

Download the ModelNet40 dataset from <a href="https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip" target="_blank">here</a>.

To train a model to classify point clouds sampled from 3D shapes:

python train_PA.py --data_dir ModelNet40_Folder

Log files and network parameters will be saved to log folder in default.

Noted that the code may be not stable, if you can help please contact me.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{li2020pointaugment,
  title={{PointAugment}: An Auto-Augmentation Framework for Point Cloud Classification},
  author={Li, Ruihui and Li, Xianzhi and Heng, Pheng-Ann and Fu, Chi-Wing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={6378--6387},
  year={2020}
}