A Novel 3d Model Recognition Approach Using Pitman-Yor Process Mixtures Of Beta-Liouville Distributions

In this paper, we formulate 3D model recognition as a statistical inference problem using a Pitman-Yor process mixture of Beta-Liouville Distributions. The proposed model is learned via a collapsed variational inference approach. Unlike classic variational Bayes, the collapsed approach does not make the non-realistic assumption that the model's parameters are independent from the assignment variables, which leads to better modelling and generalization capabilities. The merits and advantages of the proposed approach are shown via extensive experiments.