車載カメラによる道路意味情報の鳥瞰図推定
Understanding Bird's-Eye View of Road Semantics using an Onboard Camera
単一の車載カメラ映像から、道路の意味情報を鳥瞰図（BEV）としてオンライン推定する手法を提案し、画像理解・BEV理解・時間情報の統合の3要素を組み合わせて従来手法を大きく上回る性能を達成した。
著者: Yigit Baran Can, Alexander Liniger, Ozan Unal, Danda Paudel, Luc Van Gool
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, this translates to scene understanding in the bird's-eye view (BEV). However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surrounding. In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera. We study three key aspects of this task, image-level understanding, BEV level understanding, and the aggregation of temporal information. Based on these three pillars we propose a novel architecture that combines these three aspects. In our extensive experiments, we demonstrate that the considered aspects are complementary to each other for BEV understanding. Furthermore, the proposed architecture significantly surpasses the current state-of-the-art. Code: https://github.com/ybarancan/BEV_feat_stitch.
