Compressed Map Priors for 3D Perception
This addresses the problem of inefficient spatial prior usage in autonomous driving perception, offering a compressed storage solution with minimal computational overhead, though it is incremental as it builds on existing 3D perception systems.
The paper tackles the problem of autonomous vehicle vision systems lacking spatial priors from historic traversals by introducing Compressed Map Priors (CMP), a framework that learns spatial priors using a binarized hashmap requiring only 32KB/km², leading to significant and consistent improvements in 3D object detection on the nuScenes dataset across several architectures.
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.