Volumetric Part Based 3D Object Classification

摘要：

This paper proposes a volumetric part based 3D object classification approach. Superquadric-based Geon (SBG) description is implemented for representing individual volumetric parts, the constituents of 3D object. The classification of 3D object is decomposed into the constrained search on interpretation tree and the similarity measure computation. A set of integrated features and corresponding constraints are presented, which not only reflect individual parts shape, but models topological information among volumetric parts. These constraints are used to direct an efficient tree search. Following the searching stage, a similarity measure computation algorithm is developed to evaluate the shape similarity of object data and the stored models. By this classification approach, both whole and partial matching results with similarity ranks can be obtained; especially, focus match can be achieved, in which different key parts can be labeled and all the matched models with corresponding key parts can be obtained. Some experiments are given to show the validity and efficiency of the approach for 3D object classification.

关键词： 3D object classification volumetric part interpretation tree similarity measure match.

作者: Weiwei Xing Weibin Liu Baozong Yuan

作者单位: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 405-412

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）