论文标题
SEG-MAT：使用内侧轴变换的3D形状分割
SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
论文作者
论文摘要
将任意的3D对象分割成有意义的组成部分，是在各种计算机图形应用程序中遇到的基本问题。由于缺乏全球考虑，现有的3D形状分割方法由使用低级特征和零散的分割结果引起的复杂几何处理和重型计算。我们基于输入形状的内侧轴变换（MAT）提出了一种有效的方法，称为SEG-MAT。具体而言，通过在垫子中编码丰富的几何和结构信息，我们能够开发一种简单而原则的方法，以有效地识别3D形状不同部分之间的各种连接。广泛的评估和比较表明，我们的方法在分割质量方面优于最先进的方法，并且更快的速度也是一个数量级。
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.
