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.

@inproceedings{Vorba:2019:PGP:3305366.3328091,
 author = {Vorba, Ji\v{r}\'{\i} and Hanika, Johannes and Herholz, Sebastian and M\"{u}ller, Thomas and K\v{r}iv\'{a}nek, Jaroslav and Keller, Alexander},
 title = {Path Guiding in Production},
 booktitle = {ACM SIGGRAPH 2019 Courses},
 series = {SIGGRAPH '19},
 year = {2019},
 isbn = {978-1-4503-6307-5},
 location = {Los Angeles, California},
 pages = {18:1--18:77},
 articleno = {18},
 numpages = {77},
 url = {http://doi.acm.org/10.1145/3305366.3328091},
 doi = {10.1145/3305366.3328091},
 acmid = {3328091},
 publisher = {ACM},
 address = {New York, NY, USA},
}