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	Shape segmentation by hierarchical splat clustering
	Zhang, Huijuan ; Li, Chong ; Gao, Leilei ; Li, Sheng ; Wang, Guoping
	2015
关键词	Segmentation Similarity metric Patch-aware Part-aware Hierarchical clustering UNSUPERVISED CO-SEGMENTATION MESH SEGMENTATION 3D SHAPES DECOMPOSITION PARTS
英文摘要	This paper presents a novel hierarchical shape segmentation method based on splats for 3D shapes. The major contribution is to propose a new similarity metric based on splats, which combines patch-aware similarity and part-aware similarity adaptively. An optimized L-2,L-1 metric for VSA (variational shape approximation) method is used to get splats first, and such adaptive similarity metric is used to generate a hierarchy of components automatically through adaptive cluster. As a result, a binary tree is used to represent the hierarchy, in which low level segments are patch-aware regions while high level segments are part-aware components. Therefore, the combination and decomposition relations are clear between segments. Our method is designed to handle arbitrary models, such as CAD model, scanned object, organic shape, large-scale mesh and noisy model. A large number of experiments demonstrate the efficiency of our algorithm. (C) 2015 Elsevier Ltd. All rights reserved.; National Natural Science Foundation of China [61232014, 61421062, 61170205, 61173080, 61472010]; SCI(E); CPCI-S(ISTP); ,SI; 136-145; 51
语种	英语
出处	Shape Modeling International Conference (SMI 2015)
DOI标识	10.1016/j.cag.2015.05.012
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/493610]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Huijuan,Li, Chong,Gao, Leilei,et al. Shape segmentation by hierarchical splat clustering. 2015-01-01.

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