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In this paper, we propose a probabilistic method to generate abstract scenegraphs for table-top scenes from 6D object pose estimates. We explicitly makeuse of task-specfic context knowledge by encoding this knowledge as descriptiverules in Markov logic networks. Our approach to generate scene graphs isprobabilistic: Uncertainty in the object poses is addressed by a probabilisticsensor model that is embedded in a data driven MCMC process. We apply Markovlogic inference to reason about hidden objects and to detect false estimates ofobject poses. The effectiveness of our approach is demonstrated and evaluatedin real world experiments.

Table-Top Scene Analysis Using Knowledge-Supervised MCMC