  Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of
their environment, which capture both static elements of the scene such as road
layout as well as dynamic elements such as other cars and pedestrians.
Generating these map representations on the fly is a complex multi-stage
process which incorporates many important vision-based elements, including
ground plane estimation, road segmentation and 3D object detection. In this
work we present a simple, unified approach for estimating maps directly from
monocular images using a single end-to-end deep learning architecture. For the
maps themselves we adopt a semantic Bayesian occupancy grid framework, allowing
us to trivially accumulate information over multiple cameras and timesteps. We
demonstrate the effectiveness of our approach by evaluating against several
challenging baselines on the NuScenes and Argoverse datasets, and show that we
are able to achieve a relative improvement of 9.1% and 22.3% respectively
compared to the best-performing existing method.

Computer Vision and Pattern Recognition