This repo is the official PyTorch implementation for paper:
Object as Query: Lifting any 2D Object Detector to 3D Detection. Accepted by ICCV 2023.

We design Multi-View 2D Objects guided 3D Object Detector (MV2D), which can lift any 2D object detector to multi-view 3D object detection. Since 2D detections can provide valuable priors for object existence, MV2D exploits 2D detectors to generate object queries conditioned on the rich image semantics. These dynamically generated queries help MV2D to recall objects in the field of view and show a strong capability of localizing 3D objects. For the generated queries, we design a sparse cross attention module to force them to focus on the features of specific objects, which suppresses interference from noises.

This implementation is built upon PETR, and can be constructed as the install.md.

• Environments
  Linux, Python == 3.8.10, CUDA == 11.3, pytorch == 1.11.0, mmcv == 1.6.1, mmdet == 2.25.1, mmdet3d == 1.0.0, mmsegmentation == 0.28.0

• Detection Data
  Follow the mmdet3d to process the nuScenes dataset (https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/data_preparation.md).

• Pretrained weights
  We use nuImages pretrained weights from mmdetection3d. Download the pretrained weights and put them into weights/ directory.

• After preparation, you will be able to see the following directory structure:

  MV2D
  ├── mmdetection3d
  ├── configs
  ├── mmdet3d_plugin
  ├── tools
  ├── data
  │   ├── nuscenes
  │     ├── ...
  ├── weights
  ├── README.md
  

cd MV2D

You can train the model following:

bash tools/dist_train.sh configs/mv2d/exp/mv2d_r50_frcnn_two_frames_1408x512_ep24.py 8 

You can evaluate the model following:

bash tools/dist_test.sh configs/mv2d/exp/mv2d_r50_frcnn_two_frames_1408x512_ep24.py work_dirs/mv2d_r50_frcnn_two_frames_1408x512_ep24/latest.pth 8 --eval bbox

Many thanks to the authors of mmdetection3d and petr.

If you find this repo useful for your research, please cite

@article{wang2023object,
  title={Object as query: Equipping any 2d object detector with 3d detection ability},
  author={Wang, Zitian and Huang, Zehao and Fu, Jiahui and Wang, Naiyan and Liu, Si},
  journal={arXiv preprint arXiv:2301.02364},
  year={2023}
}

For questions about our paper or code, please contact Zitian Wang([email protected]).