Fast and Reliable Two-View Translation Estimation
(2014) 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 p.1606-1612- Abstract
- It has long been recognized that one of the fundamental difficulties in the estimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to classical random sampling methods, our approach is guaranteed to compute the optimal solution of a cost function based on reprojection errors and it has better time complexity. The performance is in fact independent of the inlier/outlier ratio of the data. This opens up for a more reliable approach to robust ego-motion estimation. Our basic translation estimator can be embedded into a system that computes the full camera... (More)
- It has long been recognized that one of the fundamental difficulties in the estimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to classical random sampling methods, our approach is guaranteed to compute the optimal solution of a cost function based on reprojection errors and it has better time complexity. The performance is in fact independent of the inlier/outlier ratio of the data. This opens up for a more reliable approach to robust ego-motion estimation. Our basic translation estimator can be embedded into a system that computes the full camera rotation. We demonstrate the applicability in several difficult settings with large amounts of outliers. It turns out to be particularly well-suited for small rotations and rotations around a known axis (which is the case for cellular phones where the gravitation axis can be measured). Experimental results show that compared to standard RANSAC methods based on minimal solvers, our algorithm produces more accurate estimates in the presence of large outlier ratios. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8227393
- author
- Fredriksson, Johan LU ; Enqvist, Olof and Kahl, Fredrik LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- pages
- 1606 - 1612
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
- conference location
- Columbus, OH, United States
- conference dates
- 2014-06-23 - 2014-06-28
- external identifiers
-
- wos:000361555601083
- scopus:84911434933
- ISSN
- 1063-6919
- DOI
- 10.1109/CVPR.2014.208
- language
- English
- LU publication?
- yes
- id
- b6ef6af2-d06d-4e5a-b9d9-695653bf784c (old id 8227393)
- date added to LUP
- 2016-04-01 15:01:22
- date last changed
- 2022-05-15 21:46:25
@inproceedings{b6ef6af2-d06d-4e5a-b9d9-695653bf784c,
abstract = {{It has long been recognized that one of the fundamental difficulties in the estimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to classical random sampling methods, our approach is guaranteed to compute the optimal solution of a cost function based on reprojection errors and it has better time complexity. The performance is in fact independent of the inlier/outlier ratio of the data. This opens up for a more reliable approach to robust ego-motion estimation. Our basic translation estimator can be embedded into a system that computes the full camera rotation. We demonstrate the applicability in several difficult settings with large amounts of outliers. It turns out to be particularly well-suited for small rotations and rotations around a known axis (which is the case for cellular phones where the gravitation axis can be measured). Experimental results show that compared to standard RANSAC methods based on minimal solvers, our algorithm produces more accurate estimates in the presence of large outlier ratios.}},
author = {{Fredriksson, Johan and Enqvist, Olof and Kahl, Fredrik}},
booktitle = {{2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
issn = {{1063-6919}},
language = {{eng}},
pages = {{1606--1612}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Fast and Reliable Two-View Translation Estimation}},
url = {{http://dx.doi.org/10.1109/CVPR.2014.208}},
doi = {{10.1109/CVPR.2014.208}},
year = {{2014}},
}