We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection overlook the structural constraints within linear map elements, resulting in performance degradation in long-range scenarios. In this paper, we exploit the properties of map elements to improve the performance of map construction. We extract more accurate bird's eye view (BEV) features guided by their linear structure, and then propose a hierarchical sparse map representation to further leverage the scalability of vectorized map elements and design a progressive decoding mechanism and a supervision strategy based on this representation. Our approach, ScalableMap, demonstrates superior performance on the nuScenes dataset, especially in long-range scenarios, surpassing previous state-of-the-art model by 6.5 mAP while achieving 18.3 FPS. Code is available at https://github.com/jingy1yu/ScalableMap.

翻译：我们提出了一种新颖的端到端流水线，利用车载相机传感器实现在线长距离矢量化高清（HD）地图构建。矢量化HD地图表示采用折线和多边形表征地图元素，被下游任务广泛使用。然而，以往借鉴动态目标检测设计的方案忽视了线性地图元素内的结构约束，导致在长距离场景中性能下降。本文利用地图元素特性提升地图构建性能：首先通过线性结构引导提取更精确的鸟瞰图（BEV）特征，进而提出一种层次化稀疏地图表示，以进一步利用矢量化地图元素的可扩展性，并基于该表示设计渐进式解码机制与监督策略。我们的方法ScalableMap在nuScenes数据集上展现出卓越性能，尤其在长距离场景中，以18.3 FPS的推理速度超越此前最优模型6.5 mAP。代码已开源至https://github.com/jingy1yu/ScalableMap。