  Scene understanding is a popular and challenging topic in both computer
vision and photogrammetry. Scene graph provides rich information for such scene
understanding. 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.
