MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction

Jing Yang, Minyue Jiang, Sen Yang, Xiao Tan, Yingying Li, Errui Ding, Jingdong Wang, Hanli Wang

Keywords: Online HD map construction，vectorized representation，autonomous driving

Abstract: The construction of vectorized high-definition map typically requires capturing both category and geometry information of map elements. Current state-of-the-art methods often adopt solely either point-level or instance-level representation, overlooking the strong intrinsic relationship between points and instances. In this work, we propose a simple yet efficient framework named MGMapNet (multi-granularity map network) to model map elements with multi-granularity representation, integrating both coarse-grained instance-level and fine-grained point-level queries. Specifically, these two granularities of queries are generated from the multi-scale bird's eye view features using a proposed multi-granularity aggregator. In this module, instance-level query aggregates features over the entire scope covered by an instance, and the point-level query aggregates features locally. Furthermore, a point-instance interaction module is designed to encourage information exchange between instance-level and point-level queries. Experimental results demonstrate that the proposed MGMapNet achieves state-of-the-art performances, surpassing MapTRv2 by 5.3 mAP on the nuScenes dataset and 4.4 mAP on the Argoverse2 dataset, respectively.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 5440