Practical global optimization for multiview geometry
(2006) In Lecture Notes in Computer Science 3951(Pt 1: Proceedings). p.592-605- Abstract
- This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and hemography estimation. Unlike traditional methods which may get trapped in local minima due to the non-convex nature of these problems, this approach provides a theoretical guarantee of global optimality. The formulation relies on recent developments in fractional programming and the theory of convex underestimators and allows a unified framework for minimizing the standard L-2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L-1-norm which is less sensitive to outliers. The efficacy of our algorithm is... (More)
- This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and hemography estimation. Unlike traditional methods which may get trapped in local minima due to the non-convex nature of these problems, this approach provides a theoretical guarantee of global optimality. The formulation relies on recent developments in fractional programming and the theory of convex underestimators and allows a unified framework for minimizing the standard L-2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L-1-norm which is less sensitive to outliers. The efficacy of our algorithm is empirically demonstrated by good performance on experiments for both synthetic and real data. An open source MATLAB toolbox that implements the algorithm is also made available to facilitate further research. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/405669
- author
- Agarwal, S ; Chandraker, MK ; Kahl, Fredrik LU ; Kriegman, D and Belongie, S
- organization
- publishing date
- 2006
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Lecture Notes in Computer Science
- volume
- 3951
- issue
- Pt 1: Proceedings
- pages
- 592 - 605
- publisher
- Springer
- external identifiers
-
- wos:000237552900046
- scopus:33745866266
- ISSN
- 1611-3349
- language
- English
- LU publication?
- yes
- id
- 6b4832e5-2faf-4da3-b765-b484c2c3573b (old id 405669)
- alternative location
- http://vision.ucsd.edu/kriegman-grp/papers/eccv06b.pdf
- date added to LUP
- 2016-04-01 12:02:38
- date last changed
- 2022-01-26 22:01:45
@article{6b4832e5-2faf-4da3-b765-b484c2c3573b,
abstract = {{This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and hemography estimation. Unlike traditional methods which may get trapped in local minima due to the non-convex nature of these problems, this approach provides a theoretical guarantee of global optimality. The formulation relies on recent developments in fractional programming and the theory of convex underestimators and allows a unified framework for minimizing the standard L-2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L-1-norm which is less sensitive to outliers. The efficacy of our algorithm is empirically demonstrated by good performance on experiments for both synthetic and real data. An open source MATLAB toolbox that implements the algorithm is also made available to facilitate further research.}},
author = {{Agarwal, S and Chandraker, MK and Kahl, Fredrik and Kriegman, D and Belongie, S}},
issn = {{1611-3349}},
language = {{eng}},
number = {{Pt 1: Proceedings}},
pages = {{592--605}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science}},
title = {{Practical global optimization for multiview geometry}},
url = {{http://vision.ucsd.edu/kriegman-grp/papers/eccv06b.pdf}},
volume = {{3951}},
year = {{2006}},
}