Revisiting the PnP Problem: A Fast, General and Optimal Solution
Zheng, Yinqiang ; Kuang, Yubin LU ; Sugimoto, Shigeki ; Åström, Karl LU
and Okutomi, Masatoshi
(2013)
IEEE International Conference on Computer Vision (ICCV), 2013
p.2344-2351
- Abstract
- In this paper, we revisit the classical perspective-n-point (PnP) problem, and propose the first non-iterative O(n) solution that is fast, generally applicable and globally optimal. Our basic idea is to formulate the PnP problem into a functional minimization problem and retrieve all its stationary points by using the Gr¨obner basis technique. The novelty lies in a non-unit quaternion representation to parameterize the rotation and a simple but elegant formulation of the PnP problem into an unconstrained optimization problem. Interestingly, the polynomial system arising from its first-order optimality condition assumes two-fold symmetry, a nice property that can be utilized to improve speed and numerical stability of a Gr¨obner basis... (More)
- In this paper, we revisit the classical perspective-n-point (PnP) problem, and propose the first non-iterative O(n) solution that is fast, generally applicable and globally optimal. Our basic idea is to formulate the PnP problem into a functional minimization problem and retrieve all its stationary points by using the Gr¨obner basis technique. The novelty lies in a non-unit quaternion representation to parameterize the rotation and a simple but elegant formulation of the PnP problem into an unconstrained optimization problem. Interestingly, the polynomial system arising from its first-order optimality condition assumes two-fold symmetry, a nice property that can be utilized to improve speed and numerical stability of a Gr¨obner basis solver. Experiment results have demonstrated that, in terms of accuracy, our proposed solution is definitely better than the state-ofthe- art O(n) methods, and even comparable with the reprojection error minimization method. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4249625
- author
- Zheng, Yinqiang
; Kuang, Yubin
LU
; Sugimoto, Shigeki
; Åström, Karl
LU
and Okutomi, Masatoshi
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- computer vision, pose, pnp
- host publication
- [Host publication title missing]
- pages
- 8 pages
- publisher
- Computer Vision Foundation
- conference name
- IEEE International Conference on Computer Vision (ICCV), 2013
- conference location
- Sydney, Australia
- conference dates
- 2013-12-01 - 2013-12-08
- external identifiers
-
- scopus:84898785848
- language
- English
- LU publication?
- yes
- additional info
- The authoritative version of this paper will be available i IEEE Xplore.
- id
- 796e79a4-1c65-4d8f-9bc9-e329b47e65a4 (old id 4249625)
- alternative location
- http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zheng_Revisiting_the_PnP_2013_ICCV_paper.pdf
- date added to LUP
- 2016-04-04 11:58:29
- date last changed
- 2022-05-01 21:59:48
@inproceedings{796e79a4-1c65-4d8f-9bc9-e329b47e65a4,
abstract = {{In this paper, we revisit the classical perspective-n-point (PnP) problem, and propose the first non-iterative O(n) solution that is fast, generally applicable and globally optimal. Our basic idea is to formulate the PnP problem into a functional minimization problem and retrieve all its stationary points by using the Gr¨obner basis technique. The novelty lies in a non-unit quaternion representation to parameterize the rotation and a simple but elegant formulation of the PnP problem into an unconstrained optimization problem. Interestingly, the polynomial system arising from its first-order optimality condition assumes two-fold symmetry, a nice property that can be utilized to improve speed and numerical stability of a Gr¨obner basis solver. Experiment results have demonstrated that, in terms of accuracy, our proposed solution is definitely better than the state-ofthe- art O(n) methods, and even comparable with the reprojection error minimization method.}},
author = {{Zheng, Yinqiang and Kuang, Yubin and Sugimoto, Shigeki and Åström, Karl and Okutomi, Masatoshi}},
booktitle = {{[Host publication title missing]}},
keywords = {{computer vision; pose; pnp}},
language = {{eng}},
pages = {{2344--2351}},
publisher = {{Computer Vision Foundation}},
title = {{Revisiting the PnP Problem: A Fast, General and Optimal Solution}},
url = {{https://lup.lub.lu.se/search/files/5898124/4249650.pdf}},
year = {{2013}},
}