Autonomous vehicles require road information for their operation, usually in form of HD maps. Since offline maps eventually become outdated or may only be partially available, online HD map construction methods have been proposed to infer map information from live sensor data. A key issue remains how to exploit such partial or outdated map information as a prior. We introduce M3TR (Multi-Masking Map Transformer), a generalist approach for HD map construction both with and without map priors. We address shortcomings in ground truth generation for Argoverse 2 and nuScenes and propose the first realistic scenarios with semantically diverse map priors. Examining various query designs, we use an improved method for integrating prior map elements into a HD map construction model, increasing performance by +4.3 mAP. Finally, we show that training across all prior scenarios yields a single Generalist model, whose performance is on par with previous Expert models that can handle only one specific type of map prior. M3TR thus is the first model capable of leveraging variable map priors, making it suitable for real-world deployment. Code is available at https://github.com/immel-f/m3tr

翻译：自动驾驶车辆通常需要以高精地图形式获取道路信息。由于离线地图最终会过时或可能仅部分可用，研究人员提出了在线高精地图构建方法，旨在从实时传感器数据中推断地图信息。关键问题在于如何利用此类部分或过时的地图信息作为先验。本文提出M3TR（多掩码地图Transformer），这是一种适用于有无地图先验场景的通用高精地图构建方法。我们解决了Argoverse 2和nuScenes数据集在真值生成方面的缺陷，并首次提出了具有语义多样性地图先验的真实场景。通过研究多种查询设计，我们采用改进方法将先验地图要素集成到高精地图构建模型中，使性能提升+4.3 mAP。最后，我们证明在所有先验场景上进行训练可得到单一通用模型，其性能与先前仅能处理特定类型地图先验的专家模型相当。因此，M3TR成为首个能够利用可变地图先验的模型，使其适用于实际部署。代码发布于https://github.com/immel-f/m3tr