  3D scene graph prediction is a task that aims to concurrently predict object
classes and their relationships within a 3D environment. As these environments
are primarily designed by and for humans, incorporating commonsense knowledge
regarding objects and their relationships can significantly constrain and
enhance the prediction of the scene graph. In this paper, we investigate the
application of commonsense knowledge graphs for 3D scene graph prediction on
point clouds of indoor scenes. Through experiments conducted on a real-world
indoor dataset, we demonstrate that integrating external commonsense knowledge
via the message-passing method leads to a 15.0 % improvement in scene graph
prediction accuracy with external knowledge and $7.96\%$ with internal
knowledge when compared to state-of-the-art algorithms. We also tested in the
real world with 10 frames per second for scene graph generation to show the
usage of the model in a more realistic robotics setting.
