Improving efficiency of density-based shape descriptors for 3D object retrieval

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.