MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction
Authors: Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, Jianke Zhu

Currently high-definition (HD) map construction leans towards a lightweight online generation tendency which aims to preserve timely and reliable road scene information. However map elements contain strong shape priors. Subtle and sparse annotations make current detection-based frameworks ambiguous in locating relevant feature scopes and cause the loss of detailed structures in prediction. To alleviate these problems 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 improvement of around 10 mAP for different input modalities. Extensive experiments also demonstrate that our approach showcases strong robustness and generalization capabilities. Our code can be found at https://github.com/xiaolul2/MGMap.