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

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-...

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Vydané v:	Pattern recognition Ročník 46; číslo 1; s. 449 - 461
Hlavní autori:	Lian, Zhouhui, Godil, Afzal, Bustos, Benjamin, Daoudi, Mohamed, Hermans, Jeroen, Kawamura, Shun, Kurita, Yukinori, Lavoué, Guillaume, Van Nguyen, Hien, Ohbuchi, Ryutarou, Ohkita, Yuki, Ohishi, Yuya, Porikli, Fatih, Reuter, Martin, Sipiran, Ivan, Smeets, Dirk, Suetens, Paul, Tabia, Hedi, Vandermeulen, Dirk
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.01.2013 Elsevier
Predmet:	3D shape retrieval Algorithms Applied sciences Benchmark Benchmarking Categories Computer Science Computer Vision and Pattern Recognition Detection, estimation, filtering, equalization, prediction Exact sciences and technology Finite element method Information Retrieval Information, signal and communications theory Non-rigid Pattern recognition Retrieval Signal and communications theory Signal processing Signal, noise State of the art Telecommunications and information theory Three dimensional Non-rigid 3D shape retrieval Benchmark Performance evaluation State of the art Three dimensional shape Pattern recognition Object recognition Algorithm performance Volume Object detection Web site Target detection Content analysis
ISSN:	0031-3203, 1873-5142
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Popis
Shrnutí:	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]. ► We develop a new benchmark consisting of 600 non-rigid 3D watertight meshes. ► We evaluate and compare 11 non-rigid 3D shape retrieval methods. ► We find that no single method performs best for all kinds of objects. ► Models and evaluation tools of the new benchmark are publicly available.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0031-3203 1873-5142
DOI:	10.1016/j.patcog.2012.07.014