Efficient Solvers for Minimal Problems by Syzygy-based Reduction
Larsson, Viktor LU ; Åström, Karl LU
and Oskarsson, Magnus
LU
(2017)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
p.2383-2392
- Abstract
- In this paper we study the problem of automatically generating
polynomial solvers for minimal problems. The main
contribution is a new method for finding small elimination
templates by making use of the syzygies (i.e. the polynomial
relations) that exist between the original equations. Using
these syzygies we can essentially parameterize the set
of possible elimination templates.
We evaluate our method on a wide variety of problems
from geometric computer vision and show improvement
compared to both handcrafted and automatically generated
solvers. Furthermore we apply our method on two previously
unsolved relative orientation problems.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e10f7381-3eeb-43e8-b138-5c3f8c2cde74
- author
- Larsson, Viktor
LU
; Åström, Karl
LU
and Oskarsson, Magnus
LU
- organization
- publishing date
- 2017-07
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
- pages
- 10 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
- conference location
- Honolulu, United States
- conference dates
- 2017-07-21 - 2017-07-26
- external identifiers
-
- scopus:85041930337
- ISBN
- 978-1-5386-0458-8
- 978-1-5386-0457-1
- DOI
- 10.1109/CVPR.2017.256
- project
- Semantic Mapping and Visual Navigation for Smart Robots
- language
- English
- LU publication?
- yes
- id
- e10f7381-3eeb-43e8-b138-5c3f8c2cde74
- alternative location
- http://openaccess.thecvf.com/content_cvpr_2017/papers/Larsson_Efficient_Solvers_for_CVPR_2017_paper.pdf
- date added to LUP
- 2018-01-26 22:43:34
- date last changed
- 2024-11-13 00:06:55
@inproceedings{e10f7381-3eeb-43e8-b138-5c3f8c2cde74,
abstract = {{In this paper we study the problem of automatically generating<br/>polynomial solvers for minimal problems. The main<br/>contribution is a new method for finding small elimination<br/>templates by making use of the syzygies (i.e. the polynomial<br/>relations) that exist between the original equations. Using<br/>these syzygies we can essentially parameterize the set<br/>of possible elimination templates.<br/>We evaluate our method on a wide variety of problems<br/>from geometric computer vision and show improvement<br/>compared to both handcrafted and automatically generated<br/>solvers. Furthermore we apply our method on two previously<br/>unsolved relative orientation problems.}},
author = {{Larsson, Viktor and Åström, Karl and Oskarsson, Magnus}},
booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017}},
isbn = {{978-1-5386-0458-8}},
language = {{eng}},
pages = {{2383--2392}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Efficient Solvers for Minimal Problems by Syzygy-based Reduction}},
url = {{http://dx.doi.org/10.1109/CVPR.2017.256}},
doi = {{10.1109/CVPR.2017.256}},
year = {{2017}},
}