用多粒度表示提升高精地图构建精度，兼顾整体与细节。
MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction
- 融合实例级与点级查询，捕捉地图元素的层次关系
- 在nuScenes和Argoverse2上分别领先5.3和4.4 mAP
- 适合自动驾驶高精地图生成与端到端建图任务
高精地图构建需同时捕捉地图元素的类别与几何信息。现有先进方法通常仅采用点级或实例级表示，忽略了点与实例间的内在关联。本文提出一种名为MGMapNet（多粒度地图网络）的简单高效框架，通过多粒度表示整合粗粒度实例级与细粒度点级查询。具体地，二者查询由多尺度鸟瞰图（BEV）特征经提出的多粒度聚合模块生成：实例级查询在实例覆盖范围内聚合特征，点级查询则进行局部聚合。此外，设计点实例交互模块以促进两类查询间的信息交换。实验表明，所提MGMapNet在nuScenes上超越MapTRv2达5.3 mAP，在Argoverse2上提升4.4 mAP，达到当前最优性能。
原文摘要 · Abstract (English)
The construction of Vectorized High-Definition (HD) 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 relationships between points and instances. In this work, we propose a simple yet efficient framework named MGMapNet (Multi-Granularity Map Network) to model map element with a 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 (BEV) 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 performance, surpassing MapTRv2 by 5.3 mAP on nuScenes and 4.4 mAP on Argoverse2 respectively.
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