Global Optimization through Rotation Space Search
(2009) In International Journal of Computer Vision 82(1). p.64-79- Abstract
- This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which optimal solutions have been previously given. In particular, a method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors. Recently convex optimization techniques have been shown to provide optimal solutions to many of the common problems in structure... (More)
- This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which optimal solutions have been previously given. In particular, a method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors. Recently convex optimization techniques have been shown to provide optimal solutions to many of the common problems in structure from motion. However, they do not apply to problems involving rotations. The search method described in this paper allows such problems to be solved optimally. Apart from the essential matrix, the algorithm is applied to the camera pose problem, providing an optimal algorithm. The approach has been implemented and tested on a number of both synthetically generated and real data sets with good performance. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1311470
- author
- Hartley, Richard I and Kahl, Fredrik LU
- organization
- publishing date
- 2009
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Global optimization, Essential matrix, Branch-and-bound algorithm
- in
- International Journal of Computer Vision
- volume
- 82
- issue
- 1
- pages
- 64 - 79
- publisher
- Springer
- external identifiers
-
- wos:000262986100004
- scopus:59149100446
- ISSN
- 1573-1405
- DOI
- 10.1007/s11263-008-0186-9
- language
- English
- LU publication?
- yes
- id
- b1c4e168-d744-42a0-87da-46a7a651434e (old id 1311470)
- date added to LUP
- 2016-04-01 11:37:17
- date last changed
- 2022-04-28 17:27:53
@article{b1c4e168-d744-42a0-87da-46a7a651434e,
abstract = {{This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which optimal solutions have been previously given. In particular, a method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors. Recently convex optimization techniques have been shown to provide optimal solutions to many of the common problems in structure from motion. However, they do not apply to problems involving rotations. The search method described in this paper allows such problems to be solved optimally. Apart from the essential matrix, the algorithm is applied to the camera pose problem, providing an optimal algorithm. The approach has been implemented and tested on a number of both synthetically generated and real data sets with good performance.}},
author = {{Hartley, Richard I and Kahl, Fredrik}},
issn = {{1573-1405}},
keywords = {{Global optimization; Essential matrix; Branch-and-bound algorithm}},
language = {{eng}},
number = {{1}},
pages = {{64--79}},
publisher = {{Springer}},
series = {{International Journal of Computer Vision}},
title = {{Global Optimization through Rotation Space Search}},
url = {{http://dx.doi.org/10.1007/s11263-008-0186-9}},
doi = {{10.1007/s11263-008-0186-9}},
volume = {{82}},
year = {{2009}},
}