ReVMap: Vectorized Global Mapping via Connectivity-Aware Local Map Fusion

Large-scale annotation of vectorized high-definition (HD) maps requires substantial human effort, motivating local-to-global auto-labeling that incrementally constructs global maps from local vector predictions. In this formulation, limited reliability management allows inaccurate predictions to be accumulated and reused as priors, causing persistent error propagation across iterations. We propose ReVMap, a local-to-global framework for reliable vectorized global map construction. ReVMap introduces a reliability-aware map evolution strategy that unifies uncertainty-guided prior reuse with revisable global map updates. To realize this strategy, ReVMap estimates localization uncertainty for vectorized local map elements and propagates these reliability cues to the accumulated global map. The resulting uncertainty-aware global representation guides subsequent local prediction and facilitates revisable integration through learning-based local-to-global connectivity estimation and uncertainty-aware fusion. ReVMap achieves state-of-the-art results on nuScenes and Argoverse2, with 36.1 mGAP, 43.4 miECM, and 58.0 local mAP on nuScenes, and 62.1 mGAP, 62.5 miECM, and 77.0 local mAP on Argoverse2.