3D Model Retrieval Using Probability Density-Based Shape Descriptors

Authors:
Ceyhun Burak Akgül;Bülent Sankur;Yücel Yemez;Francis Schmitt
Affiliations:
Philips Research Europe, High Tech Campus, The Netherlands;Boǧaziçi University, Istanbul;Koç University, Istanbul;Télécom ParisTech, Paris
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

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Abstract

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 non-parametric 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.