Fast solvers for minimal radial distortion relative pose problems
(2021) 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops p.3663-3672- Abstract
In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal... (More)
In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.
(Less)
- author
- Oskarsson, Magnus LU
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
- series title
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- pages
- 10 pages
- publisher
- IEEE Computer Society
- conference name
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
- conference location
- Virtual, Online, United States
- conference dates
- 2021-06-19 - 2021-06-25
- external identifiers
-
- scopus:85116041596
- ISSN
- 2160-7516
- 2160-7508
- ISBN
- 9781665448994
- DOI
- 10.1109/CVPRW53098.2021.00406
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021 IEEE.
- id
- 04b82939-e0e4-4c5a-82b6-4869a25e2f9c
- date added to LUP
- 2021-10-19 15:29:56
- date last changed
- 2024-07-27 22:47:21
@inproceedings{04b82939-e0e4-4c5a-82b6-4869a25e2f9c, abstract = {{<p>In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.</p>}}, author = {{Oskarsson, Magnus}}, booktitle = {{Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021}}, isbn = {{9781665448994}}, issn = {{2160-7516}}, language = {{eng}}, pages = {{3663--3672}}, publisher = {{IEEE Computer Society}}, series = {{IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}}, title = {{Fast solvers for minimal radial distortion relative pose problems}}, url = {{http://dx.doi.org/10.1109/CVPRW53098.2021.00406}}, doi = {{10.1109/CVPRW53098.2021.00406}}, year = {{2021}}, }