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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Veröffentlicht in:Pattern recognition Jg. 46; H. 1; S. 449 - 461
Hauptverfasser: 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
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.01.2013
Elsevier
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2012.07.014