Characterizing structural relationships in scenes using graph kernels
Modeling virtual environments is a time consuming and expensive task that is becoming increasingly popular for both professional and casual artists. The model density and complexity of the scenes representing these virtual environments is rising rapidly. This trend suggests that data mining a 3D scene corpus could be a very powerful tool enabling more efficient scene design. In this paper, we show how to represent scenes as graphs that encode models and their semantic relationships. We then define a kernel between these relationship graphs that compares common virtual substructures in two graphs and captures the similarity between their corresponding scenes. We apply this framework to several scene modeling problems, such as finding similar scenes, relevance feedback, and context based model search. We show that incorporating structural relationships allows our method to provide a more relevant set of results when compared against previous approaches to model context search.
ACM Trans. Graph.