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Adjoint-Driven Importance Sampling in Light Transport Simulation
 Vorba, Jiří ; Křivánek, Jaroslav (advisor) ; Keller, Alexander (referee) ; Wann Jensen, Henrik (referee)
Title: Adjoint-Driven Importance Sampling in Light Transport Simulation Author: RNDr. Jiří Vorba Department: Department of Software and Computer Science Education Supervisor: doc. Ing. Jaroslav Křivánek, Ph.D., Department of Software and Computer Science Education Abstract: Monte Carlo light transport simulation has recently been adopted by the movie industry as a standard tool for producing photo realistic imagery. As the industry pushes current technologies to the very edge of their possibilities, the unprecedented complexity of rendered scenes has underlined a fundamental weakness of MC light transport simulation: slow convergence in the presence of indirect illumination. The culprit of this poor behaviour is that the sam- pling schemes used in the state-of-the-art MC transport algorithms usually do not adapt to the conditions of rendered scenes. We base our work on the ob- servation that the vast amount of samples needed by these algorithms forms an abundant source of information that can be used to derive superior sampling strategies, tailored for a given scene. In the first part of this thesis, we adapt general machine learning techniques to train directional distributions for biasing scattering directions of camera paths towards incident illumination (radiance). Our approach allows progressive...
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Adjoint-Driven Importance Sampling in Light Transport Simulation
 Vorba, Jiří ; Křivánek, Jaroslav (advisor) ; Keller, Alexander (referee) ; Wann Jensen, Henrik (referee)
Title: Adjoint-Driven Importance Sampling in Light Transport Simulation Author: RNDr. Jiří Vorba Department: Department of Software and Computer Science Education Supervisor: doc. Ing. Jaroslav Křivánek, Ph.D., Department of Software and Computer Science Education Abstract: Monte Carlo light transport simulation has recently been adopted by the movie industry as a standard tool for producing photo realistic imagery. As the industry pushes current technologies to the very edge of their possibilities, the unprecedented complexity of rendered scenes has underlined a fundamental weakness of MC light transport simulation: slow convergence in the presence of indirect illumination. The culprit of this poor behaviour is that the sam- pling schemes used in the state-of-the-art MC transport algorithms usually do not adapt to the conditions of rendered scenes. We base our work on the ob- servation that the vast amount of samples needed by these algorithms forms an abundant source of information that can be used to derive superior sampling strategies, tailored for a given scene. In the first part of this thesis, we adapt general machine learning techniques to train directional distributions for biasing scattering directions of camera paths towards incident illumination (radiance). Our approach allows progressive...
Detailed record

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