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Performance prediction of point-based three-dimensional volumetric measurement systems

Graff, E. C. and Gharib, M. (2008) Performance prediction of point-based three-dimensional volumetric measurement systems. Measurement Science and Technology, 19 (7). Art. No. 075403. ISSN 0957-0233. https://resolver.caltech.edu/CaltechAUTHORS:GRAmst08

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Abstract

Point-based three-dimensional volumetric measurement systems are defined as multi-view vision systems which reconstruct a three-dimensional scene by first identifying key points on the views and then performing the reconstruction. Examples of these are defocusing digital particle image velocimetry (DDPIV) (Pereira et al 2000 Exp. Fluids 29 S78–84) and 3D particle tracking velocimetry (3DPTV) (Papantoniou and Maas 1990 5th Int. Symp. on the Application of Laser Techniques in Fluid Mechanics) which reconstruct clouds of flow tracers in order to estimate flow velocities. The reconstruction algorithms in these systems are variations of an epipolar line search. This paper presents a generalized error analysis of such methods, both in reconstruction precision (error in the reconstructed scene) and reconstruction quality (number of ambiguities or 'ghosts' produced).


Item Type:Article
Additional Information:Copyright © Institute of Physics and IOP Publishing Limited 2008. Received 14 December 2007, in final form 11 May 2008. Published 12 June 2008. Print publication: Issue 7 (July 2008). The authors would like to thank the referees, whose helpful comments served to greatly improve this paper.
Group:GALCIT
Subject Keywords:defocusing, PTV, PIV, photogrammetry, reconstruction
Issue or Number:7
Record Number:CaltechAUTHORS:GRAmst08
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:GRAmst08
Alternative URL:http://dx.doi.org/10.1088/0957-0233/19/7/075403
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10844
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:13 Jun 2008
Last Modified:03 Oct 2019 00:13

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