  Scene understanding has been of high interest in computer vision. It
encompasses not only identifying objects in a scene, but also their
relationships within the given context. With this goal, a recent line of works
tackles 3D semantic segmentation and scene layout prediction. In our work we
focus on scene graphs, a data structure that organizes the entities of a scene
in a graph, where objects are nodes and their relationships modeled as edges.
We leverage inference on scene graphs as a way to carry out 3D scene
understanding, mapping objects and their relationships. In particular, we
propose a learned method that regresses a scene graph from the point cloud of a
scene. Our novel architecture is based on PointNet and Graph Convolutional
Networks (GCN). In addition, we introduce 3DSSG, a semi-automatically generated
dataset, that contains semantically rich scene graphs of 3D scenes. We show the
application of our method in a domain-agnostic retrieval task, where graphs
serve as an intermediate representation for 3D-3D and 2D-3D matching.
