Density-Based 3D Shape Descriptors. (12th November 2006)

• Record Type: Journal Article
• Authors: Akgül Akgül, Ceyhun Burak Ceyhun Burak; Sankur Sankur, Bülent Bülent; Yemez Yemez, Yücel Yücel; Schmitt Schmitt, Francis Francis
• Other Names: Daras Daras Petros Petros Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-11-12
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/32503
• Languages: English
• ISSNs: 1687-6172

Abstract

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 efficient 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.