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Please use this identifier to cite or link to this item: http://hdl.handle.net/2005/1284

Title: A Multiview Extension Of The ICP Algorithm
Authors: Pooja, A
Advisors: Govindu, Venu Madhav
Keywords: Image Processing - Algorithms
Algorithms
Iterative Closest Point Algorithm
3D Registration
Multiview Registration
Motion Averaging
Computer Vision
ICP Algorithm
Iterative Closest Point (ICP)
Submitted Date: Jan-2010
Series/Report no.: G23610
Abstract: The Iterative Closest Point (ICP) algorithm has been an extremely popular method for 3D points or surface registration. Given two point sets, it simultaneously solves for correspondences and estimates the motion between these two point sets. However, by only registering two such views at a time, ICP fails to exploit the redundant information available in multiple views that have overlapping regions. In this thesis, a multiview extension of the ICP algorithm is provided that simultaneously averages the redundant information available in the views with overlapping regions. Variants of this method that carry out such simultaneous registration in a causal manner and that utilize the transitivity property of point correspondences are also provided. The improved accuracy in registration of these motion averaged approaches in comparison with the conventional ICP method is established through extensive experiments. In addition, the motion averaged approaches are compared with the existing multiview techniques of Bergevin et. al. and Benjemaa et. al. The results of the methods applied to the Happy Buddha and the Stanford Bunny datasets of 3D Stanford repository and to the Pooh and the Bunny datasets of the Ohio (MSU/WSU) Range Image database are also presented.
Abstract file URL: http://etd.ncsi.iisc.ernet.in/abstracts/1666/G23610-Abs.pdf
URI: http://hdl.handle.net/2005/1284
Appears in Collections:Electrical Engineering (ee)

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