Robust Point Set Registration Using Gaussian Mixture Models
Robust Point Set Registration Using Gaussian Mixture Models
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DOI：
10.1109/tpami.2010.223
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发表时间：
2011-08-01
影响因子：
23.6
通讯作者：
Vemuri, Baba C.
中科院分区：
文献类型：
--
作者：
Jian, Bing;Vemuri, Baba C.
在本文中，我们提出了一个统一的框架，刚性和非刚性点集配准问题的存在显着数量的噪声和离群值。该配准框架的核心思想是使用高斯混合模型表示输入点集。然后，点集配准的问题被重新表述为对齐两个高斯混合物的问题，使得两个相应的混合物之间的统计差异测量最小化。我们表明，流行的迭代最近点（ICP）方法[1]和该领域现有的几种点集配准方法[2]，[3]，[4]，[5]，[6]，[7]是密切相关的，可以在我们的一般框架中重新解释。我们的这个一般框架的实例是基于两个高斯混合物之间的L2距离，它具有封闭形式的表达式，从而导致一个计算效率高的注册算法。由此产生的配准算法具有固有的统计鲁棒性，具有直观的解释，并且易于实现。我们还提供了与其他强大的点集配准方法的理论和实验比较。
In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We show that the popular iterative closest point (ICP) method [1] and several existing point set registration methods [2], [3], [4], [5], [6], [7] in the field are closely related and can be reinterpreted meaningfully in our general framework. Our instantiation of this general framework is based on the the L2 distance between two Gaussian mixtures, which has the closed-form expression and in turn leads to a computationally efficient registration algorithm. The resulting registration algorithm exhibits inherent statistical robustness, has an intuitive interpretation, and is simple to implement. We also provide theoretical and experimental comparisons with other robust methods for point set registration.