缺了摄像头也能建高精地图，靠动态优先级和全景校正。
SafeMap: Robust HD Map Construction from Incomplete Observations
- 用视角重要性动态选关键区域重建
- 全景特征纠正缺失视角的鸟瞰图表示
- 插件式设计，适合现有自动驾驶系统
高精度（HD）地图构建对自动驾驶至关重要，但现有方法在多视角相机数据不完整时表现不佳。本文提出SafeMap框架，专为应对部分摄像头缺失的场景设计。该框架包含两个核心模块：基于高斯分布的透视图重建（G-PVR）模块，利用视图重要性先验，根据可用相机视图间关系动态优先处理最信息丰富的区域；以及基于知识蒸馏的鸟瞰图（BEV）修正（D-BEVC）模块，利用全景BEV特征修正由不完整观测生成的BEV表示。两者协同实现端到端地图重建与鲁棒的高精地图生成。SafeMap易于实现，可无缝集成至现有系统，提供即插即用的鲁棒性增强方案。实验表明，无论在完整还是不完整场景下，SafeMap均显著优于以往方法，展现出卓越性能与可靠性。
原文摘要 · Abstract (English)
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.
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