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...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Sébastien Granger, Xavier Pennec
• Keywords: Accurate Icp, Closest Point Algorithm, Computer Vision, ECCV 2002, Gaussian Noise, Normalized Gaussian Weights, Standard Icp Algorithm