• [2022/6/1] We release the code and models of both BEVDet and BEVDet4D!
• [2022/4/1] 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 ArXiv.
• [2022/4/1] We upgrade the BEVDet paradigm with some modifications to improve its performance and inference speed. ArXiv.
• [2021/12/23] BEVDet is now on ArXiv.

• Please follow the guidelines in the original mmdet3d for preparing the repo and dataset.

i.e. Please see getting_started.md for the basic usage of MMDetection3D. We provide guidance for quick run with existing dataset and with customized dataset for beginners. There are also tutorials for learning configuration systems, adding new dataset, designing data pipeline, customizing models, customizing runtime settings and Waymo dataset.

• Prepare dataset specific for BEVDet4D. (Note: Make sure that data preparation in nuscenes_det.md has been conducted)

cd BEVDet/
python tools/data_converter/prepare_nuscenes_for_bevdet4d.py

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}
}