Alexander Keller
Jaroslav Křivánek
Jan Novák
Anton Kaplanyan
ABSTRACT
Machine learning techniques just recently enabled dramatic improvements in both realtime and offline rendering. In this course, we introduce the basic principles of machine learning and review their relations to rendering. Besides fundamental facts like the mathematical identity of reinforcement learning and the rendering equation, we cover efficient and surprisingly elegant solutions to light transport simulation, participating media, noise removal, and anti-aliasing.
- C. Chaitanya, A. Kaplanyan, C. Schied, M. Salvi, A. Lefohn, D. Nowrouzezahrai, and T. Aila. 2017. Interactive Reconstruction of Monte Carlo Image Sequences Using a Recurrent Denoising Autoencoder. ACM Trans. Graph. 36, 4, Article 98 (July 2017), 12 pages. Google Scholar
- K. Dahm and A. Keller. 2017. Learning Light Transport the Reinforced Way, to appear in Monte Carlo and Quasi-Monte Carlo Methods 2016. CoRR abs/1701.07403 (2017). http://arxiv.org/abs/1701.07403Google Scholar
- S. Kallweit, T. Müller, B. McWilliams, M. Gross, and J. Novák. 2017. Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks. ACM Trans. Graph. (Proc. of Siggraph Asia) 36, 6, Article 231 (Nov. 2017), 11 pages. Google Scholar
- J. Vorba, O. Karlík, M. Šik, T. Ritschel, and J. Křivánek. 2014. On-line Learning of Parametric Mixture Models for Light Transport Simulation. ACM Trans. Graph. 33, 4, Article 101 (July 2014), 11 pages. Google ScholarDigital Library
Index Terms
Machine learning and rendering
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