3D indoor scenes are ubiquitous in computer graphics applications such as 3D games and interior design. With the emerging applications in VR/AR, there is an increasing demand of realistic 3D scene data. However, designing 3D indoor scenes requires proficient 3D modeling skills and is often time-consuming. A promising solution to the content-creation bottleneck of scenes is to utilize the existing scene data for data-driven 3D scene generation. Recent research about data-driven indoor scene processing in computer graphics usually takes a holistic view and operates at the object-level for scene analysis and synthesis. The main limitation of existing methods is their applicability to characterizing and modeling complex scenes. In this thesis, we address the problems of data-driven 3D indoor scene analysis and synthesis via sub-scene level processing. Our goal is to improve the understanding of scene structures through sub-scene level analysis and develop efficient systems to create complex scenes by manipulating sub-scenes instead of individual objects.To this end, we first introduce focal points, the representative sub-scenes, for characterizing, comparing, and organizing collections of complex and heterogeneous data, and apply the developed concepts and algorithms to collections of 3D indoor scenes. Then, we propose a framework for action-driven evolution of 3D indoor scenes. Human actions learned from annotated photographs are applied to trigger appropriate object placements at a sub-scene level, inducing a more compact way of scene generation. Finally, we present a novel framework that uses natural language to generate 3D indoor scenes. We demonstrate advantages of focal-centric scene comparison and organization over existing approaches. We show results of our action-driven and language-driven scene synthesis that lead to realistic, messy and complex 3D scenes, and evaluate the plausibility and naturalness of the scenes by user studies.
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Thesis advisor: Zhang, Hao
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