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3D shape retrieval using viewpoint information‐theoretic measures
Author(s) -
Bonaventura Xavier,
Guo Jianwei,
Meng Weiliang,
Feixas Miquel,
Zhang Xiaopeng,
Sbert Mateu
Publication year - 2013
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1566
Subject(s) - computer science , similarity (geometry) , mutual information , histogram , measure (data warehouse) , benchmark (surveying) , similarity measure , pooling , scalar (mathematics) , channel (broadcasting) , viewpoints , data mining , artificial intelligence , theoretical computer science , algorithm , image (mathematics) , mathematics , geometry , art , computer network , visual arts , geodesy , geography
In this paper, we present an information‐theoretic framework to compute the shape similarity between 3D polygonal models. Given a 3D model, an information channel between a sphere of viewpoints around the model and its polygonal mesh is defined to compute the specific information associated with each viewpoint. The obtained information sphere can be seen as a shape descriptor of the model. Then, given two models, their similarity is obtained by performing a registration process between the corresponding information spheres. The distance between the information histograms is also defined as a coarse measure of similarity, as well as the scalar value given by the mutual information of the channel. The performance of all these measures is tested using the Princeton Shape Benchmark database. Copyright © 2013 John Wiley & Sons, Ltd.