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