SA-BEV

[ICCV2023] SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detection

News

2023.07.14 SA-BEV is accepted by ICCV 2023. The paper is available here.

Main Results

Config	mAP	NDS	Baidu	Google
SA-BEV-R50	35.5	46.7	link	link
SA-BEV-R50-MSCT	37.0	48.8	link	link
SA-BEV-R50-MSCT-CBGS	38.7	51.2	link	link

Get Started

1. Please follow these steps to install SA-BEV.

a. Create a conda virtual environment and activate it.

conda create -n sabev python=3.8 -y
conda activate sabev

b. Install PyTorch and torchvision following the official instructions.

pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html

c. Install SA-BEV as mmdet3d.

pip install mmcv-full==1.5.3
pip install mmdet==2.27.0
pip install mmsegmentation==0.25.0
pip install -e .

2. Prepare nuScenes dataset as introduced in nuscenes_det.md and create the pkl for SA-BEV by running:

python tools/create_data_bevdet.py

3. Download nuScenes-lidarseg from nuScenes official site and put it under data/nuscenes/. Create depth and semantic labels from point cloud by running:

python tools/generate_point_label.py

4. Train and evalutate model following:

bash tools/dist_train.sh configs/sabev/sabev-r50.py 8 --no-validate
bash tools/dist_test.sh configs/sabev/sabev-r50.py work_dirs/sabev-r50/epoch_24_ema.pth 8 --eval bbox

Acknowledgement

This project is not possible without multiple great open-sourced code bases. We list some notable examples below.

Bibtex

If SA-BEV is helpful for your research, please consider citing the following BibTeX entry.

@article{zhang2023sabev,
  title={SA-BEV: Generating Semantic-Aware Bird's-Eye-View Feature for Multi-view 3D Object Detection},
  author={Jinqing, Zhang and Yanan, Zhang and Qingjie, Liu and Yunhong, Wang},
  journal={arXiv preprint arXiv:2307.11477},
  year={2023},
}

mengtan00/SA-BEV