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Jaroslav Křivánek |
Machine Learning and Rendering
SIGGRAPH 2018 Course
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Alexander Keller
NVIDIA |
Jaroslav Křivánek
Charles University, Prague Render Legion | Chaos Group |
Jan Novák
Disney Research |
Anton Kaplanyan
Oculus Research |
Marco Salvi
NVIDIA |
(hovering mouse over a presenter's name shows his bio sketch)
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Left: Simple path tracing (left) combined with simple reinforcement learning (middle) outperforms even the much more complicated Metropolis light transport algorithm (right) at the same computational budget. Right: Path guiding based on online learning of parametric mixture models dramatically increases the efficiency of light transport simulation both in simple and complex scenes.
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, and noise removal... Extended abstract
Reference
Alexander Keller, Jaroslav Křivánek, Jan Novák, Anton Kaplanyan, and Marco Salvi.
Machine learning and rendering.
ACM SIGGRAPH 2018 Courses (SIGGRAPH '18). ACM, New York, NY, USA.
DOI | BibTeX
Presented on Thursday, 16 August 20182pm - 5:15pm in East Building, Ballroom BC, Vancouver Convention Centre.
Course Notes
| 1. | From Machine Learning to Graphics and back (Alexander Keller)  slides (pdf)
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| 2. | Path guiding by machine learning (Jaroslav Křivánek)  slides (pptx) |
slides (pdf) |
notes pages
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| 3. | Deep Learning for Light Transport Simulation (Jan Novák) notes pages
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| 4. | Real-Time Neural Rendering in Image Space (Anton Kaplanyan) slides
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| 5. | Current and Future Research Topics (Marco Salvi) |