F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
Ekta U. Samani, Feng Tao, Harshavardhan R. Dasari, Sihao Ding, Ashis G. Banerjee
Abstract
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
BibTeX
@inproceedings{iros2023_f2bevbirdseyevie,
title = {F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving},
author = {Ekta U. Samani and Feng Tao and Harshavardhan R. Dasari and Sihao Ding and Ashis G. Banerjee},
booktitle = {IROS 2023},
year = {2023}
}