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Path tracing in production: part 1: modern path tracing

Published: 28 July 2019 Publication History

Abstract

In the past few years the movie industry has switched over from stochastic rasterisation approaches to using physically based light transport simulation: path tracing in production has become ubiquitous across studios. The new approach came with undisputed advantages such as consistent lighting, progressive previews, and fresh code bases. But also abandoning 30 years of experience meant some hard cuts affecting all stages such as lighting, look development, geometric modelling, scene description formats, the way we schedule for multi-threading, just to name a few. This means there is a rich set of people involved and as an expert in either one of these aspects it is easy to lose track of the big picture.
This is part I of a full-day course, and it focuses on the necessary background knowledge. In this part, we would like to provide context for everybody interested in understanding the challenges behind writing renderers intended for movie production work. In particular we will give an insight into movie production requirements for new students and academic researchers. On the other side we will lay a solid mathematical foundation to develop new ideas to solve problems in this context.
To further illustrate, part II of the course will focus on materials (acquisition and production requirements) and showcase practical efforts by prominent professionals in the field, pointing out unexpected challenges encountered in new shows and unsolved problems as well as room for future work wherever appropriate.

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MP4 File (gensub_288.mp4)

References

[1]
Animal Logic. 2019. AL_USDMaya Github Repository. https://github.com/AnimalLogic/AL_USDMaya.
[2]
Luca Fascione, Johannes Hanika, Rob Pieké, Christophe Hery, Ryusuke Villemin, Thorsten-Walther Schmidt, Christopher Kulla, Daniel Heckenberg, and André Mazzone. 2017. Path Tracing in Production - Part 2: Making Movies. In ACM SIGGRAPH 2017 Courses (SIGGRAPH '17). Article 15, 32 pages.
[3]
Pixar. 2019. Universal Scene Description. https://graphics.pixar.com/usd/docs/index.html.

Cited By

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  • (2023)Markov Chain Mixture Models for Real‐Time Direct IlluminationComputer Graphics Forum10.1111/cgf.1488142:4Online publication date: 26-Jul-2023
  • (2023)The method of multiple sampling by significance for the visualization of functionally defined scenesE3S Web of Conferences10.1051/e3sconf/202337605029376(05029)Online publication date: 31-Mar-2023
  • (2022)Automatic Feature Selection for Denoising Volumetric RenderingsComputer Graphics Forum10.1111/cgf.1458741:4(63-77)Online publication date: 30-Jul-2022
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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.

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Association for Computing Machinery

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Publication History

Published: 28 July 2019

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Cited By

View all
  • (2023)Markov Chain Mixture Models for Real‐Time Direct IlluminationComputer Graphics Forum10.1111/cgf.1488142:4Online publication date: 26-Jul-2023
  • (2023)The method of multiple sampling by significance for the visualization of functionally defined scenesE3S Web of Conferences10.1051/e3sconf/202337605029376(05029)Online publication date: 31-Mar-2023
  • (2022)Automatic Feature Selection for Denoising Volumetric RenderingsComputer Graphics Forum10.1111/cgf.1458741:4(63-77)Online publication date: 30-Jul-2022
  • (2021)Path graphsACM Transactions on Graphics10.1145/3478513.348054740:6(1-15)Online publication date: 10-Dec-2021
  • (2021)Shadow Layers for Participating MediaComputer Graphics Forum10.1111/cgf.1442941:1(190-200)Online publication date: 14-Dec-2021
  • (2021)Q‐NET: A Network for Low‐dimensional Integrals of Neural ProxiesComputer Graphics Forum10.1111/cgf.1434140:4(61-71)Online publication date: 15-Jul-2021
  • (2020)Grip and Filament: A USD-Based Lighting WorkflowACM SIGGRAPH 2020 Talks10.1145/3388767.3407350(1-2)Online publication date: 17-Aug-2020
  • (2020)Robust fitting of parallax-aware mixtures for path guidingACM Transactions on Graphics10.1145/3386569.339242139:4(147:1-147:15)Online publication date: 12-Aug-2020

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