Two-View Orthographic Epipolar Geometry : Minimal and Optimal Solvers
Oskarsson, Magnus LU
(2018)
In Journal of Mathematical Imaging and Vision
60(2).
p.163-173
- Abstract
We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be... (More)
We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be used in many instances, where the orthographic camera model isn’t generally used. A case of special interest is situations with repetitive structures, which give high amounts of outliers in the initial feature point matching.
(Less)
- author
- Oskarsson, Magnus
LU
- organization
- publishing date
- 2018-02
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Mathematical Imaging and Vision
- volume
- 60
- issue
- 2
- pages
- 163 - 173
- publisher
- Springer
- external identifiers
-
- scopus:85026905545
- ISSN
- 0924-9907
- DOI
- 10.1007/s10851-017-0753-1
- language
- English
- LU publication?
- yes
- id
- 0b2d14df-4a11-4639-887a-de2ed8101e60
- date added to LUP
- 2017-08-31 12:14:06
- date last changed
- 2022-05-02 21:59:13
@article{0b2d14df-4a11-4639-887a-de2ed8101e60,
abstract = {{<p>We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be used in many instances, where the orthographic camera model isn’t generally used. A case of special interest is situations with repetitive structures, which give high amounts of outliers in the initial feature point matching.</p>}},
author = {{Oskarsson, Magnus}},
issn = {{0924-9907}},
language = {{eng}},
number = {{2}},
pages = {{163--173}},
publisher = {{Springer}},
series = {{Journal of Mathematical Imaging and Vision}},
title = {{Two-View Orthographic Epipolar Geometry : Minimal and Optimal Solvers}},
url = {{http://dx.doi.org/10.1007/s10851-017-0753-1}},
doi = {{10.1007/s10851-017-0753-1}},
volume = {{60}},
year = {{2018}},
}