Advanced

Radially-Distorted Conjugate Translations

Pritts, James ; Kukelova, Zuzana ; Larsson, Viktor LU and Chum, Ondrej (2018) 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 p.1993-2001
Abstract

This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle imagery, which is now common from consumer cameras. The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion. The hidden-variable trick with ideal saturation is used to reformulate the constraints so that the solvers generated... (More)

This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle imagery, which is now common from consumer cameras. The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion. The hidden-variable trick with ideal saturation is used to reformulate the constraints so that the solvers generated by the Gröbner-basis method are stable, small and fast. Rectification and lens distortion are recovered from either one conjugately translated affine-covariant feature or two independently translated similarity-covariant features. The proposed solvers are used in a RANSAC-based estimator, which gives accurate rectifications after few iterations. The proposed solvers are evaluated against the state-of-the-art and demonstrate significantly better rectifcations on noisy measurements. Qualitative results on diverse imagery demonstrate high-accuracy undistortion and rectification.

(Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
article number
8578311
pages
9 pages
publisher
IEEE Computer Society
conference name
31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
conference location
Salt Lake City, United States
conference dates
2018-06-18 - 2018-06-22
external identifiers
  • scopus:85062875944
ISBN
9781538664209
DOI
10.1109/CVPR.2018.00213
language
English
LU publication?
yes
id
15758c33-868e-45ba-9a8c-e69542c59f33
date added to LUP
2019-04-01 09:42:35
date last changed
2020-12-29 01:01:50
@inproceedings{15758c33-868e-45ba-9a8c-e69542c59f33,
  abstract     = {<p>This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle imagery, which is now common from consumer cameras. The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion. The hidden-variable trick with ideal saturation is used to reformulate the constraints so that the solvers generated by the Gröbner-basis method are stable, small and fast. Rectification and lens distortion are recovered from either one conjugately translated affine-covariant feature or two independently translated similarity-covariant features. The proposed solvers are used in a RANSAC-based estimator, which gives accurate rectifications after few iterations. The proposed solvers are evaluated against the state-of-the-art and demonstrate significantly better rectifcations on noisy measurements. Qualitative results on diverse imagery demonstrate high-accuracy undistortion and rectification.</p>},
  author       = {Pritts, James and Kukelova, Zuzana and Larsson, Viktor and Chum, Ondrej},
  booktitle    = {Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018},
  isbn         = {9781538664209},
  language     = {eng},
  month        = {12},
  pages        = {1993--2001},
  publisher    = {IEEE Computer Society},
  title        = {Radially-Distorted Conjugate Translations},
  url          = {http://dx.doi.org/10.1109/CVPR.2018.00213},
  doi          = {10.1109/CVPR.2018.00213},
  year         = {2018},
}