Accurate Localization and Pose Estimation for Large 3D Models
Svärm, Linus LU ; Enqvist, Olof ; Oskarsson, Magnus LU
and Kahl, Fredrik
LU
(2014)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), 2014
p.532-539
- Abstract
- We consider the problem of localizing a novel image in a large 3D model. In principle, this is just an instance of camera pose estimation, but the scale introduces some challenging problems. For one, it makes the correspondence problem very difficult and it is likely that there will be a significant rate of outliers to handle. In this paper we use recent theoretical as well as technical advances to tackle these problems. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental... (More)
- We consider the problem of localizing a novel image in a large 3D model. In principle, this is just an instance of camera pose estimation, but the scale introduces some challenging problems. For one, it makes the correspondence problem very difficult and it is likely that there will be a significant rate of outliers to handle. In this paper we use recent theoretical as well as technical advances to tackle these problems. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite more than 99% of outlier correspondences. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5052468
- author
- Svärm, Linus
LU
; Enqvist, Olof
; Oskarsson, Magnus
LU
and Kahl, Fredrik
LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Localization Optimization Polynomial solvers Pose Estimation
- host publication
- Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), 2014
- conference location
- Columbus, Ohio, United States
- conference dates
- 2014-06-24 - 2014-06-27
- external identifiers
-
- wos:000361555600068
- scopus:84911381857
- ISSN
- 1063-6919
- DOI
- 10.1109/CVPR.2014.75
- language
- English
- LU publication?
- yes
- id
- 66323703-10d2-4193-bf47-9e4c54f3ce54 (old id 5052468)
- date added to LUP
- 2016-04-01 13:04:31
- date last changed
- 2022-03-29 05:23:12
@inproceedings{66323703-10d2-4193-bf47-9e4c54f3ce54,
abstract = {{We consider the problem of localizing a novel image in a large 3D model. In principle, this is just an instance of camera pose estimation, but the scale introduces some challenging problems. For one, it makes the correspondence problem very difficult and it is likely that there will be a significant rate of outliers to handle. In this paper we use recent theoretical as well as technical advances to tackle these problems. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite more than 99% of outlier correspondences.}},
author = {{Svärm, Linus and Enqvist, Olof and Oskarsson, Magnus and Kahl, Fredrik}},
booktitle = {{Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on}},
issn = {{1063-6919}},
keywords = {{Localization Optimization Polynomial solvers Pose Estimation}},
language = {{eng}},
pages = {{532--539}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Accurate Localization and Pose Estimation for Large 3D Models}},
url = {{http://dx.doi.org/10.1109/CVPR.2014.75}},
doi = {{10.1109/CVPR.2014.75}},
year = {{2014}},
}