  Bird's Eye View (BEV) representations are tremendously useful for
perception-related automated driving tasks. However, generating BEVs from
surround-view fisheye camera images is challenging due to the strong
distortions introduced by such wide-angle lenses. We take the first step in
addressing this challenge and introduce a baseline, F2BEV, to generate
discretized BEV height maps and BEV semantic segmentation maps from fisheye
images. F2BEV consists of a distortion-aware spatial cross attention module for
querying and consolidating spatial information from fisheye image features in a
transformer-style architecture followed by a task-specific head. We evaluate
single-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 the
IoU) than a state-of-the-art BEV generation method operating on undistorted
fisheye images. We also demonstrate discretized height map generation from
real-world fisheye images using F2BEV. Our dataset is publicly available at
https://github.com/volvo-cars/FB-SSEM-dataset
