Primary Sample Space Path Guiding

Jerry Jinfeng Guo, Pablo Bauszat, Jacco Bikker, Elmar Eisemann

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

We present a scheme for unbiased path guiding. Different from existing methods that focus on constructing structures in spatial-directional domain, we work in primary sample space. We collect records containing a few dimensions of random numbers as well as the luminance that the resulting path contributes. A multiple dimensional structure is built with collected information. After this, random numbers are drawn from this structure and is used to feed the path tracer. Using this scheme, we are able to work completely outside the rendering kernel. We demonstrate that our method is practical and can be efficient. We manage to reduce variance and reduce zero radiance paths by only working in the primary sample space.
Original languageEnglish
Title of host publicationEurographics Symposium on Rendering - Experimental Ideas and Implementations
EditorsWenzel Jakob, Toshiya Hachisuka
PublisherEurographics Association
Pages73-82
Number of pages10
ISBN (Electronic)978-3-03868-068-0
DOIs
Publication statusPublished - 2018
EventEGSR 2018: 29th Eurographics Symposium on Rendering - Karlsruhe Institute of Technology, Karlsruhe, Germany
Duration: 2 Jul 20184 Jul 2018
Conference number: 29
http://cg.ivd.kit.edu/egsr18/

Conference

ConferenceEGSR 2018
Country/TerritoryGermany
CityKarlsruhe
Period2/07/184/07/18
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • path guiding
  • realistic rendering

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