Ray Tracing Based on Reinforcement Learning

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Razavi, Mojtaba | 2020

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 52971 (02)
University: Sharif University of Technology
Department: Mathematical Sciences
Advisor(s): Moghadasi, Reza
Abstract:
We show that the equations of reinforcement learning and light transport simulation (rendering equation) are related integral equations. Based on this correspondence, a scheme to learn importance while sampling path space is derived. The new approach is demonstrated in a consistent ray tracing algorithm that uses reinforcement learning to progressively learn where light comes from. As using this information for importance sampling includes information about visibility, too, the number of light transport paths with non-zero contribution is dramatically increased, resulting in much less noisy images within a fixed time budget
Keywords:
Reinforcement Learning ; Integral Equation ; Ray Tracing ; Noise ; Rendering Equation

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