BSH-Det3D

Paper | Model [gib7] | video

[IROS 2023] BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

1. Installation

1.1 Requirements

All the codes are tested in the following environment:

• Linux (Ubuntu 20.04)
• Python 3.8
• PyTorch 1.10.1
• CUDA 11.3

1.2 Install

Our implementation is based on [OpenPCDet v0.5.2], so just follow their Installation.

2. Preparation

• During training, you should generated kitti's data including the generated complete object points as mentioned in BtcDet , download it [here (about 31GBs)] and put the zip file inside data/kitti/ .

• If you only want to test BSH-Det3D, please download the official KITTI 3D object detection dataset and organize the downloaded files as GETTING_STARTED.

3. Run Training

cd tools/

python train.py --cfg_file ./cfgs/kitti_models/voxelrcnn_bsh.yaml --batch_size 8

4. Run Testing

Model [gib7]

cd tools/

python test.py --cfg_file ./cfgs/kitti_models/voxelrcnn_bsh.yaml --batch_size 1 --ckpt ../ckpt/bsh_voxelrcnn.pth

5. Note

If you find that low GPU utilization affects the efficiency of your model's real-time performance, try using the command:

export OMP_NUM_THREADS=1

6. Acknowledgement

We sincerely appreciate the following open-source projects for providing valuable and high-quality codes:

• OpenPCDet
• BtcDet
• CenterPoint

Citation

If you find this project useful in your research, please consider cite:

@article{shen2023bsh,
  title={BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap},
  author={Shen, You and Zhang, Yunzhou and Wu, Yanmin and Wang, Zhenyu and Yang, Linghao and Coleman, Sonya and Kerr, Dermot},
  journal={arXiv preprint arXiv:2303.02000},
  year={2023}
}