BEVMap: mAP-Aware BEV Modeling for 3D Perception

Abstract

In autonomous driving applications, there is a strong preference for modeling the world in Bird's-Eye View (BEV), as it leads to improved accuracy and performance. BEV features are widely used in perception tasks since they allow fusing information from multiple views in an efficient manner. However, BEV features generated from camera images are prone to be imprecise due to the difficulty of estimating depth in the perspective view. Improper placement of BEV features limits the accuracy of downstream tasks. We introduce a method for incorporating map information to improve perspective depth estimation from 2D camera images and thereby producing geometrically- and semantically-robust BEV features. We show that augmenting the camera images with the BEV map and map-to-camera projections can compensate for the depth uncertainty. Experiments on the nuScenes dataset demonstrate that our method outperforms previous approaches using only camera images in segmentation and detection tasks.