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Bird's-eye-view (BEV) is a powerful and widely adopted representation forroad scenes that captures surrounding objects and their spatial locations,along with overall context in the scene. In this work, we focus on bird's eyesemantic segmentation, a task that predicts pixel-wise semantic segmentation inBEV from side RGB images. This task is made possible by simulators such asCarla, which allow for cheap data collection, arbitrary camera placements, andsupervision in ways otherwise not possible in the real world. There are twomain challenges to this task: the view transformation from side view to bird'seye view, as well as transfer learning to unseen domains. Existing worktransforms between views through fully connected layers and transfer learns viaGANs. This suffers from a lack of depth reasoning and performance degradationacross domains. Our novel 2-staged perception pipeline explicitly predictspixel depths and combines them with pixel semantics in an efficient manner,allowing the model to leverage depth information to infer objects' spatiallocations in the BEV. In addition, we transfer learning by abstractinghigh-level geometric features and predicting an intermediate representationthat is common across different domains. We publish a new dataset calledBEVSEG-Carla and show that our approach improves state-of-the-art by 24% mIoUand performs well when transferred to a new domain.

BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud