Awesome
[CVPR2020] On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks
Environment
Ubuntu 16.04.5 LTS
GPU RTX2080ti
Python 3.7
Install the python dependencies with
pip install -r requirements.txt
Data
- [ModelNet40] automatically downloaded
- [ShapeNet] /fxia22/pointnet.pytorch (follow the guidence for downloading)
The default path of data is '/data'.
Usage Sample
Train model
With default parameters setting, run
python train.py --data modelnet40 --model pointnet
Trained model is stored in '/checkpoints' with log in '/logs_train'.
Launch attack
If you don't want to retrain the model, download a trained model here (with ModelNet40 data, PointNet model), move it to '/checkpoints', then run
python attack.py --data modelnet40 --model pointnet --model_path 'example'
The attack log is stored in '/logs_attack'. The attack is default to be CTRI since TSI is done at the same time.
Supplementary materials
Please check here for supplementary materials mentioned in this paper.
References
- PointNet /charlesq34/pointnet, /fxia22/pointnet.pytorch
- PointNet++ /charlesq34/pointnet2, /yanx27/Pointnet_Pointnet2_pytorch
- DG-CNN /WangYueFt/dgcnn
- RS-CNN /Yochengliu/Relation-Shape-CNN
- Thompson Sampling /andrecianflone/thompson
- Adversarial Attacks /MadryLab, /YyzHarry/ME-Net