期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 31, 期 6, 页码 1117-1133出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2009.25
关键词
Shape matching; retrieval; surface representations; nonparametric statistics; geometric transformations; invariance; feature evaluation and selection; performance evaluation
资金
- BU [03A203]
- TUBITAK [103E038, 107E001]
We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.
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