Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks

Understand

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.