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Autonomous navigation requires scene understanding of the action-space tomove or anticipate events. For planner agents moving on the ground plane, suchas autonomous vehicles, this translates to scene understanding in thebird's-eye view (BEV). However, the onboard cameras of autonomous cars arecustomarily mounted horizontally for a better view of the surrounding. In thiswork, we study scene understanding in the form of online estimation of semanticBEV maps using the video input from a single onboard camera. We study three keyaspects of this task, image-level understanding, BEV level understanding, andthe aggregation of temporal information. Based on these three pillars wepropose a novel architecture that combines these three aspects. In ourextensive experiments, we demonstrate that the considered aspects arecomplementary to each other for BEV understanding. Furthermore, the proposedarchitecture significantly surpasses the current state-of-the-art. Code:https://github.com/ybarancan/BEV_feat_stitch.

Understanding Bird's-Eye View of Road Semantics using an Onboard Camera