Density-Based Shape Descriptors for 3D Object Retrieval
نویسندگان
چکیده
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 densitybased descriptors, one of its representatives reaching the top ranking position among competing methods.
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