Exploiting Coherence and Data-driven Models for Real-time Global Illumination

• Main Author: Nowrouzezahrai, Derek
• Other Authors: Fiume, Eugene
• Language: en_ca
• Published: 2010
• Subjects: Computer Graphics; Real-time Rendering; 0984
• Online Access: http://hdl.handle.net/1807/26218

Realistic computer generated images are computed by combining geometric effects, reflectance models for several captured and phenomenological materials, and real-world lighting according to mathematical models of physical light transport. Several important lighting phenomena should be considered when targeting realistic image simulation. A combination of soft and hard shadows, which arise from the interaction of surface and light geometries, provide necessary shape perception cues for a viewer. A wide variety of realistic materials, from physically-captured reflectance datasets to empirically designed mathematical models, modulate the virtual surface appearances in a manner that can further dissuade a viewer from considering the possibility of computational image synthesis over that of reality. Lastly, in many important cases, light reflects off many different surfaces before entering the eye. These secondary effects can be critical in grounding the viewer in a virtual world, since the human visual system is adapted to the physical world, where such effects are constantly in play. Simulating each of these effects is challenging due to their individual underlying complexity. The net complexity is compounded when several effects are combined. This thesis will investigate real-time approaches for simulating these effects under stringent performance and memory constraints, and with varying degrees of interactivity. In order to make these computations tractable given these added constraints, I will use data and signal analysis techniques to identify predictable patterns in the different spatial and angular signals used during image synthesis. The results of this analysis will be exploited with several analytic and data-driven mathematical models that are both efficient, and yield accurate approximations with predictable and controllable error.