Department of Computer Engineering2024-11-0920090162-882810.1109/TPAMI.2009.252-s2.0-65549149690http://dx.doi.org/10.1109/TPAMI.2009.25https://hdl.handle.net/20.500.14288/12054We 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.Computer scienceArtificial intelligenceElectrical and electronic engineeringJournal Article1939-3539265100000012Q113237