Segmentation of 3D meshes through spectral clustering
Segmentation of 3D meshes through spectral clustering
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DOI：
10.1109/pccga.2004.1348360
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发表时间：
2004-10
期刊：
影响因子：
--
通讯作者：
Rong Liu;Hao Zhang
中科院分区：
文献类型：
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作者：
Rong Liu;Hao Zhang
我们制定和应用谱聚类三维网格分割的第一次，并报告我们的初步研究结果。给定一组网格面，首先构造一个亲和矩阵，该矩阵对属于同一组的每一对面的可能性进行编码。然后，谱方法使用所选择的亲和矩阵的特征向量或其密切相关的图拉普拉斯算子来获得可以更容易地聚类的数据表示。我们开发了一种算法，有利于分割沿着凹区域，这是人类感知的启发。该算法理论上合理、高效、实现简单，在三维网格上获得了高质量的分割结果。
We formulate and apply spectral clustering to 3D mesh segmentation for the first time and report our preliminary findings. Given a set of mesh faces, an affinity matrix which encodes the likelihood of each pair of faces belonging to the same group is first constructed. Spectral methods then use selected eigenvectors of the affinity matrix or its closely related graph Laplacian to obtain data representations that can be more easily clustered. We develop an algorithm that favors segmentation along concave regions, which is inspired by human perception. Our algorithm is theoretically sound, efficient, simple to implement, andean achieve high-quality segmentation results on 3D meshes.