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Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion

Kuang, Yubin LU ; Solem, Jan Erik LU ; Kahl, Fredrik LU and Åström, Karl LU (2014) IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), 2014 In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on p.33-40
Abstract
In this paper, we study the problems of estimating relative

pose between two cameras in the presence of radial distortion.

Specifically, we consider minimal problems where

one of the cameras has no or known radial distortion. There

are three useful cases for this setup with a single unknown

distortion: (i) fundamental matrix estimation where the two

cameras are uncalibrated, (ii) essential matrix estimation

for a partially calibrated camera pair, (iii) essential matrix

estimation for one calibrated camera and one camera

with unknown focal length. We study the parameterization

of these three problems and derive fast polynomial solvers

based on... (More)
In this paper, we study the problems of estimating relative

pose between two cameras in the presence of radial distortion.

Specifically, we consider minimal problems where

one of the cameras has no or known radial distortion. There

are three useful cases for this setup with a single unknown

distortion: (i) fundamental matrix estimation where the two

cameras are uncalibrated, (ii) essential matrix estimation

for a partially calibrated camera pair, (iii) essential matrix

estimation for one calibrated camera and one camera

with unknown focal length. We study the parameterization

of these three problems and derive fast polynomial solvers

based on Gr¨obner basis methods. We demonstrate the numerical

stability of the solvers on synthetic data. The minimal

solvers have also been applied to real imagery with

convincing results. (Less)
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
in
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
pages
8 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), 2014
external identifiers
  • wos:000361555600005
  • scopus:84911369626
ISSN
1063-6919
DOI
10.1109/CVPR.2014.12
language
English
LU publication?
yes
id
d2cf23e9-e9b5-4ebc-82b7-50929cca3c75 (old id 5142567)
date added to LUP
2015-07-07 15:57:45
date last changed
2017-07-30 04:06:33
@inproceedings{d2cf23e9-e9b5-4ebc-82b7-50929cca3c75,
  abstract     = {In this paper, we study the problems of estimating relative<br/><br>
pose between two cameras in the presence of radial distortion.<br/><br>
Specifically, we consider minimal problems where<br/><br>
one of the cameras has no or known radial distortion. There<br/><br>
are three useful cases for this setup with a single unknown<br/><br>
distortion: (i) fundamental matrix estimation where the two<br/><br>
cameras are uncalibrated, (ii) essential matrix estimation<br/><br>
for a partially calibrated camera pair, (iii) essential matrix<br/><br>
estimation for one calibrated camera and one camera<br/><br>
with unknown focal length. We study the parameterization<br/><br>
of these three problems and derive fast polynomial solvers<br/><br>
based on Gr¨obner basis methods. We demonstrate the numerical<br/><br>
stability of the solvers on synthetic data. The minimal<br/><br>
solvers have also been applied to real imagery with<br/><br>
convincing results.},
  author       = {Kuang, Yubin and Solem, Jan Erik and Kahl, Fredrik and Åström, Karl},
  booktitle    = {Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
  issn         = {1063-6919},
  language     = {eng},
  pages        = {33--40},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion},
  url          = {http://dx.doi.org/10.1109/CVPR.2014.12},
  year         = {2014},
}