PointCAT

This repository provides the official PyTorch implementation of the following paper:

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition
Qidong Huang¹, Xiaoyi Dong¹, Dongdong Chen², Hang Zhou³, Weiming Zhang¹, Kui Zhang¹, Gang Hua⁴, Nenghai Yu^1
1University of Science and Technology of China, ²Microsoft Cloud AI, ³Simon Fraser University, ⁴Wormpex AI Research

Environment Setup

This code is tested with Python3.7 and CUDA = 10.3, to setup a conda environment, please use the following instructions:

conda env create -f environment.yaml
conda activate pointcat

Preparation

Download the aligned ModelNet40 dataset and ShapeNetPart dataset in their point cloud format and unzip them into your own dataset path. You can also run the bash script:

sh download.sh

the datasets will be downloaded at ./data by default.

Download the pretrained models we provided for attack evaluation and unzip them at ./checkpoint. The available models include PointNet, PointNet++, DGCNN and CurveNet.

Evaluation

You can directly evaluate our released pretrained models. For example, please run the following command for PointNet on ModelNet40:

python tester.py \
--data_path /PATH/TO/YOUR/DATASET/ \
--dataset ModelNet40 \
--defended_model pointnet_cls \
--batch_size 16 \
--mode test_normal \
--checkpoint_dir ./checkpoints/pointnet_pointcat_mn.pth

Training

To implement training for PointNet, please run the following command:

python trainer.py \
--experiment_dir pn_test \
--data_path /PATH/TO/YOUR/DATASET/ \
--dataset ModelNet40 \
--defended_model pointnet_cls \
--eps 0.04 \
--alpha 8. \
--beta 0.5 \
--use_cosine_similarity  \
--inner_loop_nums 4 \
--batch_size 64 \
--init_search_iters 500 \
--update_search_iters 10 \
--lr_fp 0.001 \
--use_multi_gpu

Citation

If you find this work useful for your research, please cite our paper:

@article{huang2022pointcat,
  title={PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition},
  author={Huang, Qidong and Dong, Xiaoyi and Chen, Dongdong and Zhou, Hang and Zhang, Weiming and Zhang, Kui and Hua, Gang and Yu, Nenghai},
  journal={arXiv preprint arXiv:2209.07788},
  year={2022}
}

License

The code is released under MIT License (see LICENSE file for details).