摘要： Grouping 3D objects into (semantically) meaningful categories is a challenging and important problem in 3D mining and shape processing. Here, we present a novel approach to categorize 3D objects. The method described in this article, is a belief-function-based approach and consists of two stages: the training stage, where 3D objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca-Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D models. 出版者： ACM 出版日期： 2013-03 出處： ACM transactions on intelligent systems and technology, 2013-03, Vol.4 (2), p.1-16 資源來源： ACM Digital Library 版權： licence_http://creativecommons.org/publicdomain/zero 識別號： ISSN: 2157-6904 識別號： EISSN: 2157-6912 識別號： DOI: 10.1145/2438653.2438668
