Iterative closest point algorithm with anisotropic weighting and its application to fine surface registration

The Iterative Closest Point (ICP) algorithm is a widely used method 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. It can be shown that the method converges to an at least local minimimum with respect to a mean-square distance metric. From a statistical point of view, the algorithm implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we (1) present the first variant of the ICP that accounts for anisotropic localization uncertainty in both shapes as well as in both steps of the algorithm and (2) show how to apply the method for robust fine registration of surface meshes. According to an evaluation on medical imaging data, the proposed method is better suited for fine surface registration than the original ICP, reducing the target registration error (TRE) for a set of targets located inside or near the mesh by 80% on average.

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