SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform

Cheng Lin, Lingjie Liu, Changjian Li, Leif Kobbelt, Bin Wang, Shiqing Xin, Wenping Wang
IEEE Transactions on Visualization and Computer Graphics

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.

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author={C. {Lin} and L. {Liu} and C. {Li} and L. {Kobbelt} and B. {Wang} and S. {Xin} and W. {Wang}},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform},

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