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Convergent iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error

Since its introduction in the early 1990s, the Iterative Closest Point (ICP) algorithm has become one of the most well-known methods for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given...

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Bibliographic Details
Main Authors: Maier-Hein, Lena (Author) , Franz, Alfred Michael (Author) , Schmidt, Mirko (Author) , Meinzer, Hans-Peter (Author)
Format: Article (Journal)
Language:English
Published: 2012
In: IEEE transactions on pattern analysis and machine intelligence
Year: 2012, Volume: 34, Issue: 8, Pages: 1520-1532
ISSN:1939-3539
DOI:10.1109/TPAMI.2011.248
Online Access:Verlag, Volltext: http://dx.doi.org/10.1109/TPAMI.2011.248
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Author Notes:L. Maier-Hein, A. M. Franz, T. R. dos Santos, M. Schmidt, M. Fangerau, H. Meinzer, and J. M. Fitzpatrick

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245 1 0 |a Convergent iterative closest-point algorithm to accomodate anisotropic and inhomogenous localization error  |c L. Maier-Hein, A. M. Franz, T. R. dos Santos, M. Schmidt, M. Fangerau, H. Meinzer, and J. M. Fitzpatrick 
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520 |a Since its introduction in the early 1990s, the Iterative Closest Point (ICP) algorithm has become one of the most well-known methods for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given the current alignment of the data and computes a rigid transformation accordingly. From a statistical point of view, however, it implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we show that this assumption may lead to errors and generalize the ICP such that it can account for anisotropic and inhomogenous localization errors. We 1) provide a formal description of the algorithm, 2) extend it to registration of partially overlapping surfaces, 3) prove its convergence, 4) derive the required covariance matrices for a set of selected applications, and 5) present means for optimizing the runtime. An evaluation on publicly available surface meshes as well as on a set of meshes extracted from medical imaging data shows a dramatic increase in accuracy compared to the original ICP, especially in the case of partial surface registration. As point-based surface registration is a central component in various applications, the potential impact of the proposed method is high. 
650 4 |a 3D models 
650 4 |a Algorithm design and analysis 
650 4 |a Algorithms 
650 4 |a Animals 
650 4 |a anisotropic localization error 
650 4 |a anisotropic weighting. 
650 4 |a Anisotropy 
650 4 |a Cameras 
650 4 |a computational geometry 
650 4 |a convergence of numerical methods 
650 4 |a convergent iterative closest-point algorithm 
650 4 |a covariance matrices 
650 4 |a Covariance matrix 
650 4 |a data alignment 
650 4 |a Diagnostic Imaging 
650 4 |a geometric alignment 
650 4 |a Head 
650 4 |a Humans 
650 4 |a ICP 
650 4 |a ICP algorithm 
650 4 |a Image Processing, Computer-Assisted 
650 4 |a image registration 
650 4 |a inhomogenous localization error 
650 4 |a isotropic Gaussian noise 
650 4 |a Iterative closest point algorithm 
650 4 |a iterative methods 
650 4 |a Measurement 
650 4 |a medical image processing 
650 4 |a medical imaging data 
650 4 |a mesh extraction 
650 4 |a mesh generation 
650 4 |a Noise 
650 4 |a partial overlapping surfaces 
650 4 |a partial surface registration 
650 4 |a point correspondences 
650 4 |a point-based registration 
650 4 |a point-based surface registration 
650 4 |a Principal Component Analysis 
650 4 |a Rabbits 
650 4 |a Registration 
650 4 |a solid modelling 
650 4 |a surface algorithms 
650 4 |a surface meshes 
650 4 |a Three dimensional displays 
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