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arXiv · 2010.11488

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

Cheng Lin·Lingjie Liu·Changjian Li·Leif Kobbelt·Bin Wang·Shiqing Xin·Wenping Wang
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Abstract

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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Cheng Lin, Lingjie Liu, Changjian Li, Leif Kobbelt, Bin Wang, Shiqing Xin, Wenping Wang. 2020-10-22. SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform. https://doi.org/10.1109/tvcg.2020.3032566

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