Abstract | ||
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Planar low-rank regions commonly found in man-made environments, can be used to estimate a rectifying homography that provides valuable information about the camera and the 3D plane they observe. Methods to recover such a homography exist, but detection of low-rank regions is largely unsolved, especially for omnidirectional cameras where significant distortions make the problem even more challenging. In this paper we address this problem as follows. First we propose a method to generate a low-rank probability map on an omnidirectional image and use it to build a training set in a self-supervised manner to train deep models to predict low-rank likelihood maps for omnidirectional images. Second, we propose to adapt regular CNN operators to equirectangular images and to combine them seamlessly into a network where each layer preserves the properties of the equirectangular representation. Finally, on the new KITTI360 dataset, we show that the rectifying homography of detected low-rank regions in such predicted maps allows to factorize out the camera-plane pose up to certain ambiguities that can be easily overcome. |
Year | DOI | Venue |
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2021 | 10.1109/CVPRW53098.2021.00408 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021) |
DocType | ISSN | Citations |
Conference | 2160-7508 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zoltan Kato | 1 | 265 | 28.28 |
Gabor Nagy | 2 | 0 | 0.34 |
Martin Humenberger | 3 | 0 | 0.34 |
Gabriela Csurka | 4 | 972 | 85.08 |