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.
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install required python packages
python3 -m pip install -r requirements.txt
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Download cama_label.zip [Google Drive]
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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
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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}
}