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SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
arXiv - CS - Graphics Pub Date : 2020-10-22 , DOI: arxiv-2010.11488
Cheng Lin, Lingjie Liu, Changjian Li, Leif Kobbelt, Bin Wang, Shiqing Xin, Wenping Wang

Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications. Existing methods for 3D shape segmentation suffer from complex geometry processing and heavy computation caused by using low-level features and fragmented segmentation results due to the lack of global consideration. We present an efficient method, called SEG-MAT, based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able to develop a simple and principled approach to effectively identify the various types of junctions between different parts of a 3D shape. Extensive evaluations and comparisons show that our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster.

中文翻译:

SEG-MAT:使用中轴变换的 3D 形状分割

将任意 3D 对象分割成具有结构意义的组成部分是广泛的计算机图形应用程序中遇到的基本问题。由于缺乏全局考虑,现有的3D形状分割方法由于使用低级特征和分割结果而导致几何处理复杂和计算量大。我们提出了一种基于输入形状的中轴变换 (MAT) 的有效方法,称为 SEG-MAT。具体来说,利用 MAT 中编码的丰富几何和结构信息,我们能够开发一种简单而有原则的方法来有效识别 3D 形状不同部分之间的各种类型的连接点。
更新日期:2020-10-23
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