Non-Rigid Point Set Registration by Preserving Global and Local Structures.
Non-Rigid Point Set Registration by Preserving Global and Local Structures.
复制标题
通过保留全局和局部结构进行非刚性点集配准
DOI：
10.1109/tip.2015.2467217
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发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Yuille AL
中科院分区：
文献类型：
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作者：
Ma J;Zhao J;Yuille AL
在之前的点配准工作中，输入点集通常使用高斯混合模型表示，然后通过概率方法解决配准问题，其目的是利用点集上的全局关系。然而，对于非刚性形状，相邻点之间的局部结构也很强且稳定，因此有助于恢复点对应关系。在本文中，我们将点配准公式化为密度混合的估计，其中使用形状上下文等局部特征来分配混合模型的隶属概率。这使我们能够在匹配过程中保留全局和局部结构。两个点集之间的变换在再生核希尔伯特空间中指定，并采用稀疏近似来实现快速实现。对合成数据和真实数据的大量实验表明，我们的方法在各种类型的失真（例如变形、噪声、离群值、旋转和遮挡）下具有鲁棒性。它大大优于最先进的方法，特别是当数据严重退化时。
In previous work on point registration, the input point sets are often represented using Gaussian mixture models and the registration is then addressed through a probabilistic approach, which aims to exploit global relationships on the point sets. For non-rigid shapes, however, the local structures among neighboring points are also strong and stable and thus helpful in recovering the point correspondence. In this paper, we formulate point registration as the estimation of a mixture of densities, where local features, such as shape context, are used to assign the membership probabilities of the mixture model. This enables us to preserve both global and local structures during matching. The transformation between the two point sets is specified in a reproducing kernel Hilbert space and a sparse approximation is adopted to achieve a fast implementation. Extensive experiments on both synthesized and real data show the robustness of our approach under various types of distortions, such as deformation, noise, outliers, rotation, and occlusion. It greatly outperforms the state-of-the-art methods, especially when the data is badly degraded.