Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
SMI '04 Proceedings of the Shape Modeling International 2004
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Density-based 3D shape descriptors
EURASIP Journal on Applied Signal Processing
Density-based shape descriptors for 3d object retrieval
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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We consider 3D shape description as a probability modeling problem. The local surface properties are first measured via various features, and then the probability density function (pdf) of the multidimensional feature vector becomes the shape descriptor. Our prior work has shown that, for 3D object retrieval, pdf-based schemes can provide descriptors that are computationally efficient and performance-wise on a par with or better than the state-of-the-art methods. In this paper, we specifically focus on discretization problems in the multidimensional feature space, selection of density evaluation points and dimensionality reduction techniques to further improve the performance of our density-based descriptors.