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.
@article{arxiv.2010.11488,
title = {SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform},
author = {Cheng Lin and Lingjie Liu and Changjian Li and Leif Kobbelt and Bin Wang and Shiqing Xin and Wenping Wang},
journal= {arXiv preprint arXiv:2010.11488},
year = {2020}
}
Comments
IEEE Transactions on Visualization and Computer Graphics (TVCG), to appear