Official Implementation of A Vision-Centric Approach for Static Map Element Annotation
Arxiv | Youtube | Bilibili

CAMA: Consistent and Accurate Map Annotation, nuScenes example:

CAMA is also used for detecting drowsiness driving patterns based on static map element matching. For instance, our proposed driving behavior dataset CAMA-D. Details here (https://github.com/FatigueView/fatigueview)

• Upload nuScenes xxx scenes from v1.0-test with CAMA labels.
• Add reprojection demo for both CAMA and nuScenes origin labels.

1. install required python packages

   python3 -m pip install -r requirements.txt  

2. Download cama_label.zip [Google Drive]

3. Modify config.yaml accordingly:

   • dataroot: path to the origin nuScenes dataset
   • converted_dataroot: output converted dataset dir
   • cama_label_file: path to cama_label.zip you just download from 2
   • output_video_dir: where the demo video writes

4. Run the pipeline

   python3 main.py --config config.yaml

If you benefit from this work, please cite the mentioned and our paper:

@inproceedings{zhang2021deep,
  title={A Vision-Centric Approach for Static Map Element Annotation},
  author={Zhang, Jiaxin and Chen, Shiyuan and Yin, Haoran and Mei, Ruohong and Liu, Xuan and Yang, Cong and Zhang, Qian and Sui, Wei},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA 2024)},
  pages={1-7}
}