DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense

Created by Hang Zhou, Kejiang Chen, Weiming Zhang, Han Fang, Wenbo Zhou, Nenghai Yu.

Introduction

This repository is for our ICCV 2019 paper DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense.

Installation

Install TensorFlow. The code has been tested with Python 3.6, TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7 on Ubuntu 16.04.

Usage

Compile sh files in directory "tf_ops/" before usage.

To process a point cloud by DUP-Net:

# Statistical outlier removal (SOR)
python filter.py --removal 'sor' \
                 --batch_size 4 \
                 --test_path 'data/lsgan_bro1_nogan2' \
                 --filtered_dir 'data/modelnet40_filtered/filtered_test'

# DUP-Net
python upsample.py --num_point 2048 \
                   --up_ratio 2 \
                   --test_path 'data/modelnet40_filtered' \
                   --upsampled_dir 'data/modelnet40_upsampled/upsampled_test'

To classify the processed point cloud:

python evaluate_filtered_targeted_adv.py --num_classes 40 \
                                         --model_path 'log/modelnet40_pointnet/model.ckpt' \
                                         --test_path 'data/modelnet40_upsampled'

Citation

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

@inproceedings{zhou2019dup,
   title={DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense},
   author={Zhou, Hang and Chen, Kejiang and Zhang, Weiming and Fang, Han and Zhou, Wenbo and Yu, Nenghai},
   booktitle={Proceedings of the IEEE International Conference on Computer Vision},
   pages={1961--1970},
   year={2019}
 }

License