Jiří Vorba
Jaroslav Křivánek
ABSTRACT
Path guiding is a family of adaptive variance reduction techniques in physically-based rendering, which includes methods for sampling both direct and indirect illumination, surfaces and volumes but also for sampling optimal path lengths and making splitting decisions. Since adoption of path tracing as a de facto standard in the VFX industry several years ago, there has been an increased interest in producing high-quality images with low amount of Monte Carlo samples per pixel. Path guiding, which has received attention in the research community in the past few years, has proven to be useful for this task and therefore has been adopted by Weta Digital. Recently, it has also been implemented in the Walt Disney Animation Studios' Hyperion and Pixar's Renderman. The goal of this course is to share our practical experience with path guiding in production and to provide self-contained overview of recently published techniques and to discuss their pros and cons. We also take audience through theoretical background of various path guiding methods which are mostly based on machine learning - used to adapt sampling distributons based on observed samples - and zero-variance random walk theory - used as a framework for combining different sampling decisions in an optimal way. At the end of our course we discuss open problems and invite researchers to further develop path guiding in their future work.
- Benedikt Bitterli, Srinath Ravichandran, Thomas Müller, Magnus Wrenninge, Jan Novák, Steve Marschner, and Wojciech Jarosz. 2018. A radiative transfer framework for non-exponential media. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia) 37, 6 (nov 2018), 225:1--225:17. Google Scholar
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- Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018. Neural Ordinary Differential Equations. arXiv: 1806.07366 (June 2018).Google Scholar
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron C. Courville. 2018. Neural Autoregressive Flows. arXiv:1804.00779 (April 2018).Google Scholar
- Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. 2018. Averaging Weights Leads to Wider Optima and Better Generalization. arXiv:1803.05407 (March 2018).Google Scholar
- Alexander Keller, Matthijs Van Keirsbilck, and Xiaodong Yang. 2019. Structural Sparsity: Speeding Up Training and Inference of Neural Networks by Linear Algorithms. In GTC Talks. https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9389-structural+sparsity%3a+speeding+up+training+and+inference+of+neural+networks+by+linear+algorithmsGoogle Scholar
- Petr Vévoda, Ivo Kondapaneni, and Jaroslav Křivánek. 2018. Bayesian online regression for adaptive direct illumination sampling. 37, 4 (Aug. 2018), 125:1--125:12. Google ScholarDigital Library
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