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Fast and Reliable Two-View Translation Estimation

Fredriksson, Johan LU ; Enqvist, Olof and Kahl, Fredrik LU (2014) 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 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)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
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
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
2015-12-01 08:50:24
date last changed
2017-07-23 04:28:57
@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},
  year         = {2014},
}