University of Oulu

S. Bhayani, T. Sattler, D. Barath, P. Beliansky, J. Heikkilä and Z. Kukelova, "Calibrated and Partially Calibrated Semi-Generalized Homographies," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 5916-5925, doi: 10.1109/ICCV48922.2021.00588.

Calibrated and partially calibrated semi-generalized homographies

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Author: Bhayani, Snehal1; Sattler, Torsten2; Barath, Daniel3;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague
3Department of Computer Science, Computer Vision and Geometry Group, ETH Zürich
4Faculty of Mathematics and Physics, Charles University, Prague
5Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: IEEE Computer Society, 2021
Publish Date: 2023-03-31


In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One group of solvers assumes the perspective camera to be fully calibrated, while the other estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structure-from-motion and visual localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. Thanks to a clever parametrization and the elimination ideal method, our solvers only need to solve a univariate polynomial of degree five or three, respectively a system of polynomial equations in two variables. All proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.

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Series: IEEE International Conference on Computer Vision
ISSN: 1550-5499
ISSN-E: 2380-7504
ISSN-L: 1550-5499
ISBN: 978-1-6654-2812-5
ISBN Print: 978-1-6654-2813-2
Pages: 5916 - 5925
DOI: 10.1109/iccv48922.2021.00588
Host publication: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Conference: IEEE International Conference on Computer Vision
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This paper was funded by the OP VVV funded project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”, the EU Horizon 2020 project RICAIP (No 857306) and the European Regional Development Fund under project IMPACT (No. CZ.02.1.01/0.0/0.0/15 003/0000468).
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