Indoor scene synthesis has become a popular topic in recent years. Synthesizing functional and plausible indoor scenes is an inherently difficult task since it requires considerable knowledge to both choose reasonable object categories and arrange objects appropriately. In this survey, we propose four criteria which group a wide range of 3D (three-dimensional) indoor scene synthesis techniques according to various aspects (specifically, four groups of categories). It also provides hints, through comprehensively comparing all the techniques to demonstrate their effectiveness and drawbacks, and discussions of potential remaining problems.

We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design, and further develop a weakly-supervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning. The proposed framework is flexible, transferable, and readable. We present a variety of computer-aided indoor scene design applications using this representation, to show the usefulness and robustness of the proposed framework.