The construction of online vectorized High-Definition (HD) maps is critical for downstream prediction and planning. Recent efforts have built strong baselines for this task, however, shapes and relations of instances in urban road systems are still under-explored, such as parallelism, perpendicular, or rectangle-shape. In our work, we propose GeMap ($\textbf{Ge}$ometry $\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map instances beyond basic perception. Specifically, we design a geometric loss based on angle and distance clues, which is robust to rigid transformations. We also decouple self-attention to independently handle Euclidean shapes and relations. Our method achieves new state-of-the-art performance on the NuScenes and Argoverse 2 datasets. Remarkably, it reaches a 71.8% mAP on the large-scale Argoverse 2 dataset, outperforming MapTR V2 by +4.4% and surpassing the 70% mAP threshold for the first time. Code is available at https://github.com/cnzzx/GeMap

翻译：在线矢量化高清（HD）地图的构建对于下游预测与规划任务至关重要。近期研究已为该任务建立了稳健的基线，然而城市道路系统中实例的形状与关系（如平行、垂直或矩形结构）仍未被充分探索。本文提出GeMap（$\textbf{Ge}$ometry $\textbf{Map}$），该方法通过端到端学习超越基础感知的地图实例欧几里得形状与关系。具体而言，我们设计了基于角度与距离线索的几何损失函数，该函数对刚性变换具有鲁棒性；同时解耦自注意力机制以独立处理欧几里得形状与关系。本方法在NuScenes与Argoverse 2数据集上均达到新最优性能。值得注意的是，在大型Argoverse 2数据集上，其mAP达到71.8%，超越MapTR V2达4.4%，并首次突破70% mAP阈值。代码已开源至https://github.com/cnzzx/GeMap。