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Scene understanding has been of high interest in computer vision. Itencompasses not only identifying objects in a scene, but also theirrelationships within the given context. With this goal, a recent line of workstackles 3D semantic segmentation and scene layout prediction. In our work wefocus on scene graphs, a data structure that organizes the entities of a scenein 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 sceneunderstanding, mapping objects and their relationships. In particular, wepropose a learned method that regresses a scene graph from the point cloud of ascene. Our novel architecture is based on PointNet and Graph ConvolutionalNetworks (GCN). In addition, we introduce 3DSSG, a semi-automatically generateddataset, that contains semantically rich scene graphs of 3D scenes. We show theapplication of our method in a domain-agnostic retrieval task, where graphsserve as an intermediate representation for 3D-3D and 2D-3D matching.

Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions