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

• URL: http://arxiv.org/abs/2010.11488v1
• Date: Thu, 22 Oct 2020 07:15:23 GMT
• Title: SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
• Authors: Cheng Lin, Lingjie Liu, Changjian Li, Leif Kobbelt, Bin Wang, Shiqing Xin, Wenping Wang
• Abstract summary: We present an efficient method for 3D shape segmentation 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 identify the various types of junctions between different parts of a 3D shape. Our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster.
• Score: 49.51977253452456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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