Abstract
An integral part of driver assistance technology is surround-view (SV), a system which uses four fisheye (wide-angle) cameras on the front, right, rear, and left sides of a vehicle to completely capture the surroundings. Inherent in SV are four wide-baseline orthogonally-divergent fisheye stereo systems, from which, depth information may be extracted and used in 3D scene understanding. Traditional stereo approaches typically require fisheye distortion removal and stereo rectification for efficient correspondence matching. However, such approaches suffer from loss of data and cannot account for widely disparate appearances of objects in corresponding views. We introduce a novel method for computing depth from fisheye stereo that uses an understanding of the underlying lens models and a convolutional network to predict correspondences. We also built a synthetic database for developing and testing fisheye stereo and SV algorithms. We demonstrate the performance of our depth estimation method on this database.
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Pan, J., Mueller, M., Lahlou, T., Bovik, A.C. (2018). Orthogonally-Divergent Fisheye Stereo. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_10
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DOI: https://doi.org/10.1007/978-3-030-01449-0_10
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