Feature sensitive mesh segmentation with mean shift
Feature sensitive mesh segmentation with mean shift
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DOI：
10.1109/smi.2005.21
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
H. Yamauchi;Seungyong Lee;Yunjin Lee;Y. Ohtake;A. Belyaev;H. Seidel
中科院分区：
文献类型：
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作者：
H. Yamauchi;Seungyong Lee;Yunjin Lee;Y. Ohtake;A. Belyaev;H. Seidel
特征敏感的网格分割在许多计算机图形学和几何建模应用中是重要的。在本文中，我们开发了一种网格分割方法，这是能够产生高质量的形状分割。它尊重精细的形状特征，并适用于各种类型的形状，包括自然形状和机械零件。该方法结合了一个程序聚类网格法线的网格澄清技术的修改。对于网格法线的聚类，我们采用Mean Shift，这是一种用于聚类分散数据的强大通用技术。我们证明了我们的方法的优势，通过比较它与两个国家的最先进的网格分割技术。
Feature sensitive mesh segmentation is important for many computer graphics and geometric modeling applications. In this paper, we develop a mesh segmentation method, which is capable of producing high-quality shape partitioning. It respects fine shape features and works well on various types of shapes, including natural shapes and mechanical parts. The method combines a procedure for clustering mesh normals with a modification of the mesh clarification technique. For clustering of mesh normals, we adopt Mean Shift, a powerful general purpose technique for clustering scattered data. We demonstrate advantages of our method by comparing it with two state-of-the-art mesh segmentation techniques.