Density-based shape descriptors for 3d object retrieval

Authors:
Ceyhun Burak Akgül;Bülent Sankur;Francis Schmitt;Yücel Yemez
Affiliations:
Department of Electrical and Electronics Engineering, Boğaziçi University, Istanbul, Turkey;Department of Electrical and Electronics Engineering, Boğaziçi University, Istanbul, Turkey;GET-Télécom – CNRS UMR, France;Department of Computer Engineering, Koç University, Istanbul, Turkey
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

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Abstract

We develop a probabilistic framework that computes 3D shape descriptors in a more rigorous and accurate manner than usual histogram-based methods for the purpose of 3D object retrieval. We first use a numerical analytical approach to extract the shape information from each mesh triangle in a better way than the sparse sampling approach. These measurements are then combined to build a probability density descriptor via kernel density estimation techniques, with a rule-based bandwidth assignment. Finally, we explore descriptor fusion schemes. Our analytical approach reveals the true potential of density-based descriptors, one of its representatives reaching the top ranking position among competing methods.