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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Akgül, C.B., Sankur, B., Schmitt, F., Yemez, Y. (2006). Density-Based Shape Descriptors for 3D Object Retrieval. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_43
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DOI: https://doi.org/10.1007/11848035_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
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