Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error
Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error
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DOI：
10.1109/tpami.2011.248
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发表时间：
2012-08-01
影响因子：
23.6
通讯作者：
Fitzpatrick, J. Michael
中科院分区：
文献类型：
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作者：
Maier-Hein, Lena;Franz, Alfred M.;Fitzpatrick, J. Michael
自20世纪90年代初引入以来，迭代最近点（ICP）算法已成为3D模型几何对齐的最知名方法之一。给定由两个点集表示的两个大致对齐的形状，该算法迭代地建立给定数据的当前对齐的点对应，并相应地计算刚性变换。然而，从统计学的角度来看，它隐含地假设这些点是用各向同性高斯噪声观察的。在本文中，我们表明，这种假设可能会导致错误和推广ICP，使它可以占各向异性和非均匀的定位误差。我们1）提供了一个正式的描述的算法，2）将其扩展到部分重叠的表面注册，3）证明其收敛性，4）推导出所需的协方差矩阵的一组选定的应用程序，和5）目前的手段，优化运行时。公开可用的表面网格，以及从医学成像数据中提取的一组网格上的评价显示出显着增加的准确性相比，原来的ICP，特别是在部分表面配准的情况下。由于基于点的表面配准是各种应用中的核心组成部分，因此所提出的方法的潜在影响很大。
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.