标题： SafeMap：从不完整观测中构建鲁棒的高精地图 显示英文标题

标题： SafeMap: Robust HD Map Construction from Incomplete Observations

摘要： 鲁棒的高清（HD）地图构建对于自动驾驶至关重要，但现有方法在处理不完整的多视角摄像头数据时常常遇到困难。 本文提出了SafeMap，一种专门设计的框架，即使某些摄像头视角缺失也能确保准确性。 SafeMap集成了两个关键组件：基于高斯的透视视图重建（G-PVR）模块和基于蒸馏的鸟瞰图（BEV）校正（D-BEVC）模块。 G-PVR利用视图重要性的先验知识，根据可用摄像头视图之间的关系动态优先考虑最有信息量的区域。 此外，D-BEVC利用全景BEV特征来校正从不完整观测中得到的BEV表示。 这些组件共同促进了端到端的地图重建和鲁棒的HD地图生成。 SafeMap易于实现，并能无缝集成到现有系统中，为增强鲁棒性提供即插即用的解决方案。 实验结果表明，SafeMap在完整和不完整场景下均显著优于之前的方法，突显了其优越的性能和可靠性。 显示英文摘要

摘要： Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMap, a novel framework specifically designed to secure accuracy even when certain camera views are missing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruction (G-PVR) module and the Distillation-based Bird's-Eye-View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relationships among available camera views. Furthermore, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these components facilitate the end-to-end map reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into existing systems, offering a plug-and-play solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outperforms previous methods in both complete and incomplete scenarios, highlighting its superior performance and reliability.