How hard is 3-view triangulation really?
(2005) IEEE International Conference on Computer Vision, 2005 p.686-693- Abstract
- We present a solution for optimal triangulation in three views. The solution is guaranteed to find the optimal solution because it computes all the stationary points of the (maximum likelihood) objective function. Internally, the solution is found by computing roots of multivariate polynomial equations, directly solving the conditions for stationarity. The solver makes use of standard methods from computational commutative algebra to convert the root-finding problem into a 47
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/616506
- author
- Stewenius, Henrik LU ; Schaffalitzky, F and Nister, D
- organization
- publishing date
- 2005
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- scene geometry, image motion analysis, nonsymmetric eigenproblem, root-finding problem, computational commutative algebra, multivariate polynomial equation, maximum likelihood objective function, 3-view triangulation, optimal triangulation
- host publication
- Proceedings. Tenth IEEE International Conference on Computer Vision
- pages
- 686 - 693
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Computer Vision, 2005
- conference location
- Beijing, China
- conference dates
- 2005-10-17 - 2005-10-21
- external identifiers
-
- wos:000233155100089
- scopus:33745968244
- ISBN
- 0-7695-2334-X
- DOI
- 10.1109/ICCV.2005.115
- language
- English
- LU publication?
- yes
- id
- e68f971b-e24e-4a96-89f1-ac6be6117236 (old id 616506)
- date added to LUP
- 2016-04-04 12:23:20
- date last changed
- 2022-01-29 23:19:38
@inproceedings{e68f971b-e24e-4a96-89f1-ac6be6117236, abstract = {{We present a solution for optimal triangulation in three views. The solution is guaranteed to find the optimal solution because it computes all the stationary points of the (maximum likelihood) objective function. Internally, the solution is found by computing roots of multivariate polynomial equations, directly solving the conditions for stationarity. The solver makes use of standard methods from computational commutative algebra to convert the root-finding problem into a 47}}, author = {{Stewenius, Henrik and Schaffalitzky, F and Nister, D}}, booktitle = {{Proceedings. Tenth IEEE International Conference on Computer Vision}}, isbn = {{0-7695-2334-X}}, keywords = {{scene geometry; image motion analysis; nonsymmetric eigenproblem; root-finding problem; computational commutative algebra; multivariate polynomial equation; maximum likelihood objective function; 3-view triangulation; optimal triangulation}}, language = {{eng}}, pages = {{686--693}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{How hard is 3-view triangulation really?}}, url = {{http://dx.doi.org/10.1109/ICCV.2005.115}}, doi = {{10.1109/ICCV.2005.115}}, year = {{2005}}, }