Robust Optimal Pose Estimation
(2008) 10th European Conference on Computer Vision (ECCV 2008) 5302. p.141-153- Abstract
- We study the problem of estimating the position and orientation of a calibrated camera from an image of a known scene. A common problem in camera pose estimation is the existence of false correspondences between image features and modeled 3D points. Existing techniques Such as RANSAC to handle outliers have no guarantee of optimality. In contrast, we work with a natural extension of the L-infinity norm to the outlier case. Using a simple result from classical geometry, we derive necessary conditions for L-infinity optimality and show how to use them in a branch and bound setting to find the optimum and to detect outliers. The algorithm has been evaluated on synthetic as well as real data showing good empirical performance. In addition, for... (More)
- We study the problem of estimating the position and orientation of a calibrated camera from an image of a known scene. A common problem in camera pose estimation is the existence of false correspondences between image features and modeled 3D points. Existing techniques Such as RANSAC to handle outliers have no guarantee of optimality. In contrast, we work with a natural extension of the L-infinity norm to the outlier case. Using a simple result from classical geometry, we derive necessary conditions for L-infinity optimality and show how to use them in a branch and bound setting to find the optimum and to detect outliers. The algorithm has been evaluated on synthetic as well as real data showing good empirical performance. In addition, for cases with no outliers, we demonstrate shorter execution times than existing optimal algorithms. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1308802
- author
- Enqvist, Olof LU and Kahl, Fredrik LU
- organization
- publishing date
- 2008
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Computer Vision – ECCV 2008 (Lecture Notes in Computer Science)
- volume
- 5302
- pages
- 141 - 153
- publisher
- Springer
- conference name
- 10th European Conference on Computer Vision (ECCV 2008)
- conference location
- Marseille, France
- conference dates
- 2008-10-12 - 2008-10-18
- external identifiers
-
- wos:000260656000011
- scopus:56749169075
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 978-3-540-88681-5
- DOI
- 10.1007/978-3-540-88682-2_12
- language
- English
- LU publication?
- yes
- id
- 2c49f085-bafe-493a-959c-7067a36f609a (old id 1308802)
- date added to LUP
- 2016-04-01 12:12:14
- date last changed
- 2024-06-04 11:40:16
@inproceedings{2c49f085-bafe-493a-959c-7067a36f609a,
abstract = {{We study the problem of estimating the position and orientation of a calibrated camera from an image of a known scene. A common problem in camera pose estimation is the existence of false correspondences between image features and modeled 3D points. Existing techniques Such as RANSAC to handle outliers have no guarantee of optimality. In contrast, we work with a natural extension of the L-infinity norm to the outlier case. Using a simple result from classical geometry, we derive necessary conditions for L-infinity optimality and show how to use them in a branch and bound setting to find the optimum and to detect outliers. The algorithm has been evaluated on synthetic as well as real data showing good empirical performance. In addition, for cases with no outliers, we demonstrate shorter execution times than existing optimal algorithms.}},
author = {{Enqvist, Olof and Kahl, Fredrik}},
booktitle = {{Computer Vision – ECCV 2008 (Lecture Notes in Computer Science)}},
isbn = {{978-3-540-88681-5}},
issn = {{1611-3349}},
language = {{eng}},
pages = {{141--153}},
publisher = {{Springer}},
title = {{Robust Optimal Pose Estimation}},
url = {{http://dx.doi.org/10.1007/978-3-540-88682-2_12}},
doi = {{10.1007/978-3-540-88682-2_12}},
volume = {{5302}},
year = {{2008}},
}