Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction

Robots operating in domestic environments need to deal with a variety of different objects. Often, these objects are neither placed randomly, nor independently of each other. For example, objects on a breakfast table such as plates, knives, or bowls typically occur in recurrent configurations. In this paper, we propose a novel hierarchical generative model to reason about latent object constellations in a scene. The proposed model is a combination of Dirichlet processes and beta processes, which allow for a probabilistic treatment of the unknown dimensionality of the parameter space. We show how the model can be employed to address a set of different tasks in scene understanding ranging from unsupervised scene segmentation to completion of a partially specified scene. We describe how sampling in this model can be done using Markov chain Monte Carlo (MCMC) techniques and present an experimental evaluation with simulated as well as real-world data obtained with a Kinect camera.