Practical Robust Two-View Translation Estimation
(2015) 28th Conference on Computer Vision and Pattern Recognition, 2015 p.2684-2690- Abstract
- Outliers pose a problem in all real structure from motion
systems. Due to the use of automatic matching methods
one has to expect that a (sometimes very large) portion of
the detected correspondences can be incorrect. In this paper
we propose a method that estimates the relative translation
between two cameras and simultaneously maximizes
the number of inlier correspondences.
Traditionally, outlier removal tasks have been addressed
using RANSAC approaches. However, these are random in
nature and offer no guarantees of finding a good solution.
If the amount of mismatches is large, the approach becomes
costly because of the need to evaluate a... (More) - Outliers pose a problem in all real structure from motion
systems. Due to the use of automatic matching methods
one has to expect that a (sometimes very large) portion of
the detected correspondences can be incorrect. In this paper
we propose a method that estimates the relative translation
between two cameras and simultaneously maximizes
the number of inlier correspondences.
Traditionally, outlier removal tasks have been addressed
using RANSAC approaches. However, these are random in
nature and offer no guarantees of finding a good solution.
If the amount of mismatches is large, the approach becomes
costly because of the need to evaluate a large number of
random samples. In contrast, our approach is based on the
branch and bound methodology which guarantees that an
optimal solution will be found. While most optimal methods
trade speed for optimality, the proposed algorithm has competitive
running times on problem sizes well beyond what is
common in practice. Experiments on both real and synthetic
data show that the method outperforms state-of-the-art alternatives,
including RANSAC, in terms of solution quality.
In addition, the approach is shown to be faster than
RANSAC in settings with a large amount of outliers. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5276657
- author
- Fredriksson, Johan LU ; Larsson, Viktor LU and Olsson, Carl LU
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Two-view estimation, computer vision, optimization
- host publication
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- pages
- 7 pages
- publisher
- Computer Vision Foundation
- conference name
- 28th Conference on Computer Vision and Pattern Recognition, 2015
- conference location
- Boston, United States
- conference dates
- 2015-06-08 - 2015-06-10
- external identifiers
-
- scopus:84959249513
- ISBN
- 978-1-4673-6964-0
- DOI
- 10.1109/CVPR.2015.7298884
- language
- English
- LU publication?
- yes
- additional info
- The URL links to the open access version of the paper, provided by the Computer Vision Foundation. The authoritative version of this paper will be avavailable in IEEE Xplore.
- id
- a68dd0a6-edd3-4f92-af85-683396b0c302 (old id 5276657)
- alternative location
- http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Fredriksson_Practical_Robust_Two-View_2015_CVPR_paper.pdf
- date added to LUP
- 2016-04-04 10:53:39
- date last changed
- 2022-09-06 09:57:21
@inproceedings{a68dd0a6-edd3-4f92-af85-683396b0c302,
abstract = {{Outliers pose a problem in all real structure from motion<br/><br>
systems. Due to the use of automatic matching methods<br/><br>
one has to expect that a (sometimes very large) portion of<br/><br>
the detected correspondences can be incorrect. In this paper<br/><br>
we propose a method that estimates the relative translation<br/><br>
between two cameras and simultaneously maximizes<br/><br>
the number of inlier correspondences.<br/><br>
Traditionally, outlier removal tasks have been addressed<br/><br>
using RANSAC approaches. However, these are random in<br/><br>
nature and offer no guarantees of finding a good solution.<br/><br>
If the amount of mismatches is large, the approach becomes<br/><br>
costly because of the need to evaluate a large number of<br/><br>
random samples. In contrast, our approach is based on the<br/><br>
branch and bound methodology which guarantees that an<br/><br>
optimal solution will be found. While most optimal methods<br/><br>
trade speed for optimality, the proposed algorithm has competitive<br/><br>
running times on problem sizes well beyond what is<br/><br>
common in practice. Experiments on both real and synthetic<br/><br>
data show that the method outperforms state-of-the-art alternatives,<br/><br>
including RANSAC, in terms of solution quality.<br/><br>
In addition, the approach is shown to be faster than<br/><br>
RANSAC in settings with a large amount of outliers.}},
author = {{Fredriksson, Johan and Larsson, Viktor and Olsson, Carl}},
booktitle = {{2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
isbn = {{978-1-4673-6964-0}},
keywords = {{Two-view estimation; computer vision; optimization}},
language = {{eng}},
pages = {{2684--2690}},
publisher = {{Computer Vision Foundation}},
title = {{Practical Robust Two-View Translation Estimation}},
url = {{https://lup.lub.lu.se/search/files/5646111/5276663.pdf}},
doi = {{10.1109/CVPR.2015.7298884}},
year = {{2015}},
}