Publication:
Density-based shape descriptors for 3D object retrieval

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Akgul, Ceyhun Burak
Sankur, Bulent
Schmitt, Francis

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English

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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.

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Multimedia Content Representation, Classification and Security

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Springer-Verlag Berlin

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Computer science, information systems, Computer science, theory and methods

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