Simultaneous Multiple Rotation Averaging using Lagrangian Duality
(2013) 11th Asian Conference on Computer Vision (ACCV 2012), 2012 7726. p.245-258- Abstract
- Multiple rotation averaging is an important problem in computer vision. The problem is challenging because of the nonlinear constraints required to represent the set of rotations. To our knowledge no one has proposed any globally optimal solution for the case of simultaneous updates of the rotations. In this paper we propose a simple procedure based on Lagrangian duality that can be used to verify global optimality of a local solution, by solving a linear system of equations. We show experimentally on real and synthetic data that unless the noise levels are extremely high this procedure always generates the globally optimal solution.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3327279
- author
- Fredriksson, Johan LU and Olsson, Carl LU
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- computer vision, rotation averaging, optimization, duality
- host publication
- Lecture Notes in Computer Science (Computer Vision - ECCV 2012, 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III)
- volume
- 7726
- pages
- 14 pages
- publisher
- Springer
- conference name
- 11th Asian Conference on Computer Vision (ACCV 2012), 2012
- conference dates
- 2012-11-05 - 2012-11-09
- external identifiers
-
- scopus:84875894004
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 978-3-642-37430-2 (print)
- 978-3-642-37431-9 (online)
- language
- English
- LU publication?
- yes
- id
- 3b6b3d06-54ca-4096-ad4b-bd864e1af762 (old id 3327279)
- alternative location
- http://link.springer.com/chapter/10.1007/978-3-642-37431-9_19
- date added to LUP
- 2016-04-01 10:08:09
- date last changed
- 2024-08-11 14:08:12
@inproceedings{3b6b3d06-54ca-4096-ad4b-bd864e1af762, abstract = {{Multiple rotation averaging is an important problem in computer vision. The problem is challenging because of the nonlinear constraints required to represent the set of rotations. To our knowledge no one has proposed any globally optimal solution for the case of simultaneous updates of the rotations. In this paper we propose a simple procedure based on Lagrangian duality that can be used to verify global optimality of a local solution, by solving a linear system of equations. We show experimentally on real and synthetic data that unless the noise levels are extremely high this procedure always generates the globally optimal solution.}}, author = {{Fredriksson, Johan and Olsson, Carl}}, booktitle = {{Lecture Notes in Computer Science (Computer Vision - ECCV 2012, 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III)}}, isbn = {{978-3-642-37430-2 (print)}}, issn = {{0302-9743}}, keywords = {{computer vision; rotation averaging; optimization; duality}}, language = {{eng}}, pages = {{245--258}}, publisher = {{Springer}}, title = {{Simultaneous Multiple Rotation Averaging using Lagrangian Duality}}, url = {{http://link.springer.com/chapter/10.1007/978-3-642-37431-9_19}}, volume = {{7726}}, year = {{2013}}, }