  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.
