用压缩地图先验提升自动驾驶3D感知，让系统像老司机一样认路。
Compressed Map Priors for 3D Perception
- 用二值化哈希表存储历史路径信息，每平方公里仅需32KB。
- 在nuScenes数据集上，多种3D检测模型性能均有显著且稳定提升。
- 可无缝接入主流3D感知系统，几乎不增加计算开销。
人类驾驶员很少行驶从未有人走过的路——每天成千上万的人使用繁忙的城市道路，却只有一人能称为第一个。自动驾驶视觉系统也是如此。绝大多数部署区域都已被访问过。然而，当前多数自动驾驶视觉系统仍表现得像首次到达该位置。本文提出压缩地图先验（Compressed Map Priors, CMP），一种从历史行驶轨迹中学习空间先验的简单而有效框架。该方法使用二值化哈希表，每平方公里仅需32KB存储，相比密集存储降低20倍。压缩地图先验可轻松集成至主流3D感知系统，几乎不增加额外计算成本，并在nuScenes数据集上对多种架构的3D目标检测性能带来显著且一致的提升。
原文摘要 · Abstract (English)
Human drivers rarely travel where no person has gone before. After all, thousands of drivers use busy city roads every day, and only one can claim to be the first. The same holds for autonomous computer vision systems. The vast majority of the deployment area of an autonomous vision system will have been visited before. Yet, most autonomous vehicle vision systems act as if they are encountering each location for the first time. In this work, we present Compressed Map Priors (CMP), a simple but effective framework to learn spatial priors from historic traversals. The map priors use a binarized hashmap that requires only $32\text{KB}/\text{km}^2$, a $20\times$ reduction compared to the dense storage. Compressed Map Priors easily integrate into leading 3D perception systems at little to no extra computational costs, and lead to a significant and consistent improvement in 3D object detection on the nuScenes dataset across several architectures.
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