Projective Least-Squares: Global Solutions with Local Optimization
(2009) IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops, 2009 p.1216-1223- Abstract
- Recent work in multiple view geometry has focused on obtaining globally optimal solutions at the price of computational time efficiency. On the other hand, traditional bundle adjustment algorithms have been found to provide good solutions even though there may be multiple local minima. In this paper we justify this observation by giving a simple sufficient condition for global optimality that can be used to verify that a solution obtained from any local method is indeed global. The method is tested on numerous problem instances of both synthetic and real data sets. In the vast majority of cases we are able to verify that the solutions are optimal, in particular for small-scale problems. We also develop a branch and bound procedure that... (More)
- Recent work in multiple view geometry has focused on obtaining globally optimal solutions at the price of computational time efficiency. On the other hand, traditional bundle adjustment algorithms have been found to provide good solutions even though there may be multiple local minima. In this paper we justify this observation by giving a simple sufficient condition for global optimality that can be used to verify that a solution obtained from any local method is indeed global. The method is tested on numerous problem instances of both synthetic and real data sets. In the vast majority of cases we are able to verify that the solutions are optimal, in particular for small-scale problems. We also develop a branch and bound procedure that goes beyond verification. In cases where the sufficient condition does not hold, the algorithm returns either of the following two results: (i) a certificate of global optimality for the local solution or (ii) the global solution. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1628606
- author
- Olsson, Carl LU ; Kahl, Fredrik LU and Hartley, Richard
- organization
- publishing date
- 2009
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- CVPR: 2009 IEEE Conference on Computer Vision and Pattern Recognition
- pages
- 1216 - 1223
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
- conference location
- Miami Beach, FL, United States
- conference dates
- 2009-06-20 - 2009-06-25
- external identifiers
-
- wos:000279038000156
- scopus:70450203323
- ISSN
- 1063-6919
- DOI
- 10.1109/CVPR.2009.5206864
- language
- English
- LU publication?
- yes
- id
- 96f82c79-ba4c-4516-8238-441751555c6e (old id 1628606)
- date added to LUP
- 2016-04-01 13:08:32
- date last changed
- 2022-04-21 20:02:03
@inproceedings{96f82c79-ba4c-4516-8238-441751555c6e, abstract = {{Recent work in multiple view geometry has focused on obtaining globally optimal solutions at the price of computational time efficiency. On the other hand, traditional bundle adjustment algorithms have been found to provide good solutions even though there may be multiple local minima. In this paper we justify this observation by giving a simple sufficient condition for global optimality that can be used to verify that a solution obtained from any local method is indeed global. The method is tested on numerous problem instances of both synthetic and real data sets. In the vast majority of cases we are able to verify that the solutions are optimal, in particular for small-scale problems. We also develop a branch and bound procedure that goes beyond verification. In cases where the sufficient condition does not hold, the algorithm returns either of the following two results: (i) a certificate of global optimality for the local solution or (ii) the global solution.}}, author = {{Olsson, Carl and Kahl, Fredrik and Hartley, Richard}}, booktitle = {{CVPR: 2009 IEEE Conference on Computer Vision and Pattern Recognition}}, issn = {{1063-6919}}, language = {{eng}}, pages = {{1216--1223}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Projective Least-Squares: Global Solutions with Local Optimization}}, url = {{http://dx.doi.org/10.1109/CVPR.2009.5206864}}, doi = {{10.1109/CVPR.2009.5206864}}, year = {{2009}}, }