Adv3D

This repo is based on BEVDet to perform adversarial attack.

Get Started

Installation and Data Preparation

Step 0. Download and install Miniconda from the official website.

Step 1. Create a conda environment or use your existing one.

conda create --name adv3d python=3.10 -y
conda activate adv3d

Step 2. Install PyTorch following official instructions, e.g.

On GPU platforms:

conda install pytorch torchvision -c pytorch

Installation

We recommend that users follow our best practices to install MMDetection3D. However, the whole process is highly customizable. See Customize Installation section for more information.

Best Practices

Assuming that you already have CUDA 11.0 installed, here is a full script for quick installation of MMDetection3D with conda. Otherwise, you should refer to the step-by-step installation instructions in the next section.

pip install openmim
mim install mmcv-full
mim install mmdet
mim install mmsegmentation
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -e .

Step 0. Install MMCV using MIM.

Step 1. Install MMDetection.

pip install mmdet

Optionally, you could also build MMDetection from source in case you want to modify the code:

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
git checkout v2.24.0  # switch to v2.24.0 branch
pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

Step 2. Install MMSegmentation.

pip install mmsegmentation

Optionally, you could also build MMSegmentation from source in case you want to modify the code:

git clone https://github.com/open-mmlab/mmsegmentation.git
cd mmsegmentation
git checkout v0.20.0  # switch to v0.20.0 branch
pip install -e .  # or "python setup.py develop"

Step 3. Clone the MMDetection3D repository.

git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d

Step 4. Install build requirements and then install MMDetection3D.

pip install -v -e .  # or "python setup.py develop"

Step 5. Prepare nuScenes dataset as introduced in nuscenes_det.md and create the pkl for BEVDet by running:

python tools/create_data_bevdet.py

Step 5. Download pretrained bevdet checkpoint and place it into ./bev_model

Train model

bash tools/dist_command.sh  configs/bevdet/bevdet-r50-target.py  bev_model/bevdet-r50.pth  1  tools/attk_r50.py  --eval bbox

Visualize the predicted result.

bash tools/dist_command.sh  configs/bevdet/bevdet-r50-target.py  bev_model/bevdet-r50.pth  1  tools/attk_loca_importance_pixelcount.py  --eval bbox

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{li2023adv3d,
  title={Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF},
  author={Li, Leheng and Lian, Qing and Chen, Ying-Cong},
  journal={arXiv preprint arXiv:2309.01351},
  year={2023}
}