Radially Distorted Homographies, Revisited
This work addresses a key challenge in geometric computer vision for tasks involving real images with lens distortions, offering incremental improvements in speed for homography estimation.
The paper tackles the problem of estimating homographies with radial distortion in computer vision, providing a unified approach for three distinct distortion configurations and developing new minimal solvers that are faster than existing state-of-the-art methods while maintaining similar accuracy.
Homographies are among the most prevalent transformations occurring in geometric computer vision and projective geometry, and homography estimation is consequently a crucial step in a wide assortment of computer vision tasks. When working with real images, which are often afflicted with geometric distortions caused by the camera lens, it may be necessary to determine both the homography and the lens distortion-particularly the radial component, called radial distortion-simultaneously to obtain anything resembling useful estimates. When considering a homography with radial distortion between two images, there are three conceptually distinct configurations for the radial distortion; (i) distortion in only one image, (ii) identical distortion in the two images, and (iii) independent distortion in the two images. While these cases have been addressed separately in the past, the present paper provides a novel and unified approach to solve all three cases. We demonstrate how the proposed approach can be used to construct new fast, stable, and accurate minimal solvers for radially distorted homographies. In all three cases, our proposed solvers are faster than the existing state-of-the-art solvers while maintaining similar accuracy. The solvers are tested on well-established benchmarks including images taken with fisheye cameras. The source code for our solvers will be made available in the event our paper is accepted for publication.