Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
Date
2021-01
Authors
Lochman, Yaroslava
Dobosevych, Oles
Hryniv, Rostyslav
Pritts, James
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute orientation. We use constraints provided by orthogonal scene planes to recover the focal length. We show that solvers using feature combinations can recover more accurate calibrations than solvers using only one feature type on scenes that have a balance of lines and texture. We also show that the proposed solvers are complementary and can be used together in a RANSAC-based estimator to improve auto-calibration accuracy. State-of-the-art performance is demonstrated on a standard dataset of lens-distorted urban images. The code is available at https://github.com/ylochman/single-view-autocalib
Description
Keywords
, Camera Auto-calibration, Lens Undistorsion, Affine Rectification
Citation
Yaroslava Lochman, Oles Dobosevych, Rostyslav Hryniv, James Pritts. Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 2887-2896 https://www.doi.org/10.1109/WACV48630.2021.00293