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Fast solvers for minimal radial distortion relative pose problems

Oskarsson, Magnus LU orcid (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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
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
2024-04-06 10:59:49
@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}},
}