On Support Relations and Semantic Scene Graphs

Rapid development of robots and autonomous vehicles requires semantic information about the surrounding scene to decide upon the correct action or to be able to complete particular tasks. Scene understanding provides the necessary semantic interpretation by semantic scene graphs. For this task, so-called support relationships which describe the contextual relations between parts of the scene such as floor, wall, table, etc, need be known. This paper presents a novel approach to infer such relations and then to construct the scene graph. Support relations are estimated by considering important, previously ignored information: the physical stability and the prior support knowledge between object classes. In contrast to previous methods for extracting support relations, the proposed approach generates more accurate results, and does not require a pixel-wise semantic labeling of the scene. The semantic scene graph which describes all the contextual relations within the scene is constructed using this information. To evaluate the accuracy of these graphs, multiple different measures are formulated. The proposed algorithms are evaluated using the NYUv2 database. The results demonstrate that the inferred support relations are more precise than state-of-the-art. The scene graphs are compared against ground truth graphs.