Density-based 3D shape descriptors

  • Authors:
  • Ceyhun Burak Akgül;Bülent Sankur;Yücel Yemez;Francis Schmitt

  • Affiliations:
  • Electrical and Electronics Engineering Department, Boǧaziçi University, Bebek, Istanbul, Turkey and GET-Telecom Paris, CNRS UMR, Paris Cedex, France;Electrical and Electronics Engineering Department, Boǧaziçi University, Bebek, Istanbul, Turkey;Computer Engineering Department, Koç University, Sariyer, Istanbul, Turkey;GET-Telecom Paris, CNRS UMR, Paris Cedex, France

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2007

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Abstract

We propose a novel probabilistic framework for the extraction of density-based 3D shape descriptors using kernel density estimation. Our descriptors are derived from the probability density functions (pdf) of local surface features characterizing the 3D object geometry. Assuming that the shape of the 3D object is represented as a mesh consisting of triangles with arbitrary size and shape, we provide effcient means to approximate the moments of geometric features on a triangle basis. Our framework produces a number of 3D shape descriptors that prove to be quite discriminative in retrieval applications. We test our descriptors and compare them with several other histogram-based methods on two 3D model databases, Princeton Shape Benchmark and Sculpteur, which are fundamentally different in semantic content and mesh quality. Experimental results show that our methodology not only improves the performance of existing descriptors, but also provides a rigorous framework to advance and to test new ones.