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ECCV
2002
Springer

Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration

9 years 7 months ago
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
We investigate in this article the rigid registration of large sets of points, generally sampled from surfaces. We formulate this problem as a general Maximum-Likelihood (ML) estimation of the transformation and the matches. We show that, in the specific case of a Gaussian noise, it corresponds to the Iterative Closest Point algorithm (ICP) with the Mahalanobis distance. Then, considering matches as a hidden variable, we obtain a slightly more complex criterion that can be efficiently solved using ExpectationMaximization (EM) principles. In the case of a Gaussian noise, this new methods corresponds to an ICP with multiple matches weighted by normalized Gaussian weights, giving birth to the EM-ICP acronym of the method. The variance of the Gaussian noise is a new parameter that can be viewed as a "scale or blurring factor" on our point clouds. We show that EMICP robustly aligns the barycenters and inertia moments with a high variance, while it tends toward the accurate ICP for...
Sébastien Granger, Xavier Pennec
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2002
Where ECCV
Authors Sébastien Granger, Xavier Pennec
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