Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction
Dominik Joho, Gian Diego Tipaldi, Nikolas Engelhard, Cyrill Stachniss, Wolfram Burgard · 2012
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.