We observe that prior work on scene synthesis is divided into two camps: object-oriented approaches (which reason about the set of objects in a scene and their configurations) and space-oriented approaches (which reason about what objects occupy what regions of space). We present a new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks.
