Divide and Conquer: Improving Multi-Camera 3D Perception With 2D Semantic-Depth Priors and Input-Dependent Queries [TIP 2024]

Introduction

This repository is an official implementation of SDTR

We propose SDTR, a transformer-based framework that incorporates semantic and depth priors to improve the capability of inferring both semantic categories and 3D positions. SDTR got accepted by TIP (IEEE Transactions on Image Processing).

Preparation

Environment

This implementation is built upon PETR, and the environment preparation stage can be found in this repo (or see install.md).

Pretrained weights

We provide the pre-trained weights of backbone networks and our SDTR at gdrive. These weights have already been grouped into ckpts and work_dirs folders.

Training Data

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

• Segmentation Data
  Download Map expansion from nuScenes dataset (https://www.nuscenes.org/nuscenes#download). Extract the contents (folders basemap, expansion and prediction) to your nuScenes maps folder.
  Then build Segmentation dataset:

  cd tools
  python build-dataset.py
  

  Or download the processed data ( HDmaps.tar ) provided by PETR.

Auxiliary Supervision Data

Edit the scripts/configs.py file, first setting the TASK_Flag to choose the type of labels you want, and then setting the DATA_DIR and SAVE_DIR entries to the location of the NuScenes dataset and the desired ground truth folder respectively.

• Prepare depth labels

  python scripts/gen_depth_gt.py
  

• Prepare segmentation labels

  python scripts/gen_pvseg_gt.py
  

After preparation, the directory will be as follows:

SDTR
├── mmdetection3d
├── projects
│   ├── configs
│   ├── mmdet3d_plugin
├── tools
├── data
│   ├── nuscenes
│     ├── pv_labels
│     ├    ├── det
│     ├    ├── depth
│     ├    ├── ...
│     ├── HDmaps-nocover
│     ├── ...
├── ckpts
├── README.md

Train & inference

You can train the model following:

tools/dist_train.sh projects/configs/sdtr/sdtr_r50dcn_gridmask_p4.py 8 --work-dir work_dirs/sdtr_r50dcn_gridmask_p4/

You can evaluate the model following:

tools/dist_test.sh projects/configs/sdtr/sdtr_r50dcn_gridmask_p4.py work_dirs/sdtr_r50dcn_gridmask_p4/latest.pth 8 --eval bbox

Visualize

You can generate the reault json following:

./tools/dist_test.sh projects/configs/sdtr/sdtr_r50dcn_gridmask_p4.py work_dirs/sdtr_r50dcn_gridmask_p4/latest.pth 8 --out work_dirs/pp-nus/results_eval.pkl --format-only --eval-options 'jsonfile_prefix=work_dirs/pp-nus/results_eval'

You can visualize the 3D object detection following:

python3 tools/visualize.py

Main Results

config	NDS	mAP
SDTR-r50-p4-1408x512	38.4%	33.1%
SDTR-vov-p4-1408x512	48.2%	43.0%

Acknowledgement

Many thanks to the authors of mmdetection3d and PETR .

Citation

If you find this project useful for your research, please consider citing:

@article{song2024divide,
  title={Divide and Conquer: Improving Multi-Camera 3D Perception With 2D Semantic-Depth Priors and Input-Dependent Queries},
  author={Song, Qi and Hu, Qingyong and Zhang, Chi and Chen, Yongquan and Huang, Rui},
  journal={IEEE Transactions on Image Processing},
  year={2024},
  publisher={IEEE}
}

Contact

If you have any questions, feel free to open an issue or contact us at [email protected].