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Robust Three-View Triangulation Done Fast
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
2014 (English)In: Proceedings: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, IEEE , 2014, p. 152-157Conference paper, Published paper (Refereed)
Abstract [en]

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

Place, publisher, year, edition, pages
IEEE , 2014. p. 152-157
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
Keywords [en]
Nonlinear optimization; Structure from motion; Three-view Triangulation; Cameras; Computer vision; Conferences; Noise; Polynomials; Robustness; Three-dimensional displays
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-111512DOI: 10.1109/CVPRW.2014.28ISI: 000349552300023ISBN: 978-1-4799-4309-8 (print)OAI: oai:DiVA.org:liu-111512DiVA, id: diva2:756974
Conference
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 23-28, Columbus, OH, USA
Available from: 2014-10-20 Created: 2014-10-20 Last updated: 2018-10-09Bibliographically approved

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Hedborg, JohanRobinson, AndreasFelsberg, Michael

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CiteExportLink to record
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