BEVDet

News

• 2022.06.01 We release the code and models of both BEVDet and BEVDet4D!
• 2022.04.01 We propose BEVDet4D to lift the scalable BEVDet paradigm from the spatial-only 3D space to the spatial-temporal 4D space. Technical report is released on arixv. [BEVDet4D].
• 2022.04.01 We upgrade the BEVDet paradigm with some modifications to improve its performance and inference speed. Thchnical report of BEVDet has been updated. [BEVDetv1].
• 2021.12.23 BEVDet is now on arxiv. [BEVDet].

Update

• 2022.07.08 Spport visualization remotely! Please refer to Get Started for usage.
• 2022.06.29 Spport acceleration of the Lift-Splat-Shoot view transformer! Please refer to [Technical Report] for detailed introduction and Get Started for testing BEVDet with acceleration.

Main Results

Method	mAP	NDS	FPS	Mem (MB)	Model	Log
BEVDet-Tiny	30.8	40.4	15.6	11,965	google / baidu	google /baidu
BEVDet4D-Tiny	33.8	47.6	15.5	11,557	google / baidu	google /baidu

Get Started

Installation and Data Preparation

Please see getting_started.md

Estimate the inference speed of BEVDet

# with acceleration
python tools/analysis_tools/benchmark.py configs/bevdet/bevdet-sttiny-accelerated.py $checkpoint
# without acceleration
python tools/analysis_tools/benchmark.py configs/bevdet/bevdet-sttiny.py $checkpoint

Visualize the predicted result with open3d.

Official implementation. (Visualization locally only)

python tools/test.py $config $checkpoint --show --show-dir $save-path

Private implementation. (Visualization remotely/locally)

python tools/test.py $config $checkpoint --format-only --eval-options jsonfile_prefix=$savepath
python tools/analysis_tools/vis.py $savepath/pts_bbox/results_nusc.json

Acknowledgement

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

• open-mmlab
• CenterPoint
• Lift-Splat-Shoot
• Swin Transformer

Beside, there are some other attractive works extend the boundary of BEVDet.

• BEVerse for multi-task learning.
• BEVFusion for acceleration, multi-task learning, and multi-sensor fusion. (Note: The acceleration method is a concurrent work of that of BEVDet and has some superior characteristics like memory saving and completely equivalent.)

Bibtex

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

@article{huang2022bevdet4d,
  title={BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection},
  author={Huang, Junjie and Huang, Guan},
  journal={arXiv preprint arXiv:2203.17054},
  year={2022}
}

@article{huang2021bevdet,
  title={BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View},
  author={Huang, Junjie and Huang, Guan and Zhu, Zheng and Yun, Ye and Du, Dalong},
  journal={arXiv preprint arXiv:2112.11790},
  year={2021}
}