A comparison of methods for non-rigid 3D shape retrieval

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Lian, Zhouhui
Godil, Afzal
Bustos, Benjamin
Daoudi, Mohamed
Hermans, Jeroen
Kawamura, Shun
Kurita, Yukinori
Lavoue, Guillaume
Nguyen, Hien Van
Ohbuchi, Ryutarou

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Pergamon-Elsevier Ltd

Abstract

Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1].

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Pattern Recognition

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2037-12-31