skip to main content
10.1145/3305366.3328091acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
course

Path guiding in production

Published: 28 July 2019 Publication History

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.

References

[1]
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.
[2]
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018. Neural Ordinary Differential Equations. arXiv: 1806.07366 (June 2018).
[3]
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron C. Courville. 2018. Neural Autoregressive Flows. arXiv:1804.00779 (April 2018).
[4]
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).
[5]
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+algorithms
[6]
Petr Vévoda, Ivo Kondapaneni, and Jaroslav Křivánek. 2018. Bayesian online regression for adaptive direct illumination sampling. 37, 4 (Aug. 2018), 125:1--125:12.

Cited By

View all
  • (2024)Efficient Neural Path Guiding with 4D ModelingSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687687(1-11)Online publication date: 3-Dec-2024
  • (2024)MARS: Multi-sample Allocation through Russian roulette and SplittingSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687636(1-10)Online publication date: 3-Dec-2024
  • (2024)Cache Points for Production-Scale Occlusion-Aware Many-Lights Sampling and Volumetric ScatteringProceedings of the 2024 Digital Production Symposium10.1145/3665320.3670993(1-19)Online publication date: 24-Jul-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGGRAPH '19: ACM SIGGRAPH 2019 Courses
July 2019
3772 pages
ISBN:9781450363075
DOI:10.1145/3305366
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2019

Check for updates

Qualifiers

  • Course

Conference

SIGGRAPH '19
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)125
  • Downloads (Last 6 weeks)14
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Efficient Neural Path Guiding with 4D ModelingSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687687(1-11)Online publication date: 3-Dec-2024
  • (2024)MARS: Multi-sample Allocation through Russian roulette and SplittingSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687636(1-10)Online publication date: 3-Dec-2024
  • (2024)Cache Points for Production-Scale Occlusion-Aware Many-Lights Sampling and Volumetric ScatteringProceedings of the 2024 Digital Production Symposium10.1145/3665320.3670993(1-19)Online publication date: 24-Jul-2024
  • (2024)Real-Time Path Guiding Using Bounding Voxel SamplingACM Transactions on Graphics10.1145/365820343:4(1-14)Online publication date: 19-Jul-2024
  • (2024)Conditional Mixture Path Guiding for Differentiable RenderingACM Transactions on Graphics10.1145/365813343:4(1-11)Online publication date: 19-Jul-2024
  • (2024)Specular PolynomialsACM Transactions on Graphics10.1145/365813243:4(1-13)Online publication date: 19-Jul-2024
  • (2024)Online Neural Path Guiding with Normalized Anisotropic Spherical GaussiansACM Transactions on Graphics10.1145/364931043:3(1-18)Online publication date: 9-Apr-2024
  • (2024)NeuPreSS: Compact Neural Precomputed Subsurface Scattering for Distant Lighting of Heterogeneous Translucent ObjectsComputer Graphics Forum10.1111/cgf.1523443:7Online publication date: 18-Oct-2024
  • (2024)Bridge Sampling for Connections via Multiple Scattering EventsComputer Graphics Forum10.1111/cgf.1516043:4Online publication date: 24-Jul-2024
  • (2024)Photon Field Networks for Dynamic Real-Time Volumetric Global IlluminationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332710730:1(975-985)Online publication date: 1-Jan-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media