BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks

The official release of BEV-SAN is now available.

Updates!!

• 【2023/10/24】 We publish the code base of our work. Updating soon.
• 【2023/02/24】 Our work has been accepted by the CVPR2023.

Quick Start

Installation

Step 0. Install pytorch(v1.9.0).

Step 1. Install MMDetection3D(v1.0.0rc4).

Step 2. Install requirements.

pip install -r requirements.txt

Step 3. Install BEVDepth(gpu required).

python setup.py develop

Data Preparation

Step 0. Download nuScenes official dataset.

Step 1. Symlink the dataset root to ./data/.

ln -s [nuscenes root] ./data/

The directory will be as follows.

BEVDepth
├── data
│   ├── nuScenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
|   |   ├── v1.0-trainval

Step 2. Prepare infos.

python scripts/gen_info.py

Step 3. Prepare depth gt.

python scripts/gen_depth_gt.py

Tutorials

Train.

python [EXP_PATH] --amp_backend native -b 8 --gpus 8

Eval.

python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 8 --gpus 8

**

Cite BEV-SAN

If you use BEV-SAN in your research, please cite our work by using the following BibTeX entry:

Thanks

Our code is based on the BEVDepth(https://github.com/Megvii-BaseDetection/BEVDepth)

@misc{chi2022bevsan,
      title={BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks}, 
      author={Xiaowei Chi and Jiaming Liu and Ming Lu and Rongyu Zhang and Zhaoqing Wang and Yandong Guo and Shanghang Zhang},
      year={2022},
      eprint={2212.01231},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}