Minimal Solvers for Relative Pose with a Single Unknown Radial Distortion
(2014) IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), 2014 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:
https://lup.lub.lu.se/record/5142567
- author
- Kuang, Yubin LU ; Solem, Jan Erik LU ; Kahl, Fredrik LU and Åström, Karl LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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
- conference location
- Columbus, Ohio, United States
- conference dates
- 2014-06-24 - 2014-06-27
- 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
- 2016-04-01 13:43:10
- date last changed
- 2022-03-14 01:31:38
@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}}, doi = {{10.1109/CVPR.2014.12}}, year = {{2014}}, }