RoadBEV: Road Surface Reconstruction in Bird's Eye View
Tong Zhao Lei Yang Yichen Xie Mingyu Ding Masayoshi Tomizuka Yintao Wei
Road surface conditions, especially geometry profiles, enormously affect
driving performance of autonomous vehicles. Vision-based online road
reconstruction promisingly captures road information in advance. Existing
solutions like monocular depth estimation and stereo matching suffer from
modest performance. The recent technique of Bird's-Eye-View (BEV) perception
provides immense potential to more reliable and accurate reconstruction. This
paper uniformly proposes two simple yet effective models for road elevation
reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo, which estimate
road elevation with monocular and stereo images, respectively. The former
directly fits elevation values based on voxel features queried from image view,
while the latter efficiently recognizes road elevation patterns based on BEV
volume representing discrepancy between left and right voxel features.
Insightful analyses reveal their consistence and difference with perspective
view. Experiments on real-world dataset verify the models' effectiveness and
superiority. Elevation errors of RoadBEV-mono and RoadBEV-stereo achieve 1.83cm
and 0.56cm, respectively. The estimation performance improves by 50\% in BEV
based on monocular image. Our models are promising for practical applications,
providing valuable references for vision-based BEV perception in autonomous
driving. The code is released at https://github.com/ztsrxh/RoadBEV.