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Bird's Eye View (BEV) representations are tremendously useful forperception-related automated driving tasks. However, generating BEVs fromsurround-view fisheye camera images is challenging due to the strongdistortions introduced by such wide-angle lenses. We take the first step inaddressing this challenge and introduce a baseline, F2BEV, to generatediscretized BEV height maps and BEV semantic segmentation maps from fisheyeimages. F2BEV consists of a distortion-aware spatial cross attention module forquerying and consolidating spatial information from fisheye image features in atransformer-style architecture followed by a task-specific head. We evaluatesingle-task and multi-task variants of F2BEV on our synthetic FB-SSEM dataset,all of which generate better BEV height and segmentation maps (in terms of theIoU) than a state-of-the-art BEV generation method operating on undistortedfisheye images. We also demonstrate discretized height map generation fromreal-world fisheye images using F2BEV. Our dataset is publicly available athttps://github.com/volvo-cars/FB-SSEM-dataset

F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving