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MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction

Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, Jianke Zhu

[Paper] (arXiv). CVPR2024

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Video Demo

<p align="center"> <a href="https://youtu.be/woTOaVPmHYQ"><img src="assets/video_demo.png" width="75%"></a> </p>

Introduction

We propose MGMap, a mask-guided approach that effectively highlights the informative regions and achieves precise map element localization by introducing the learned masks. Specifically, MGMap employs learned masks based on the enhanced multi-scale BEV features from two perspectives. At the instance level, we propose the Mask-activated instance (MAI) decoder, which incorporates global instance and structural information into instance queries by the activation of instance masks. At the point level, a novel position-guided mask patch refinement (PG-MPR) module is designed to refine point locations from a finer-grained perspective, enabling the extraction of point-specific patch information. Compared to the baselines, our proposed MGMap achieves a notable promotion of around 10 mAP for different input modalities. Extensive experiments also demonstrate that our approach showcases strong robustness and generalization capabilities.

<img src="./assets/MGMap.jpg" width="800px">

TODO

Getting Started

Quantitative Results

nuScenes dataset

ModelModalityBackboneEpochmAPFPSConfigDownload
MGMapCameraR503061.411.6configmodel
MGMapLidarSecond2467.95.5configmodel
MGMapCamera&LidarR50&Sec2471.74.8configmodel

Acknowledgements

MGMap is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: BEVFormer, HDMapNet, MapTR, SparseInst.

Citation

If the paper and code help your research, please kindly cite:

@misc{liu2024mgmap,
      title={MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction}, 
      author={Xiaolu Liu and Song Wang and Wentong Li and Ruizi Yang and Junbo Chen and Jianke Zhu},
      year={2024},
      eprint={2404.00876},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}