Primary Sample Space Path Guiding

Conference paper (2018)

Authors

Jerry Jinfeng Guo Computer Graphics and Visualisation - EEMCS
P. Bauszat Computer Graphics and Visualisation - EEMCS
J Bikker Universiteit Utrecht
E. Eisemann Computer Graphics and Visualisation - EEMCS
Research Group
Computer Graphics and Visualisation (EEMCS) (TU Delft)
Copyright: © 2018 Jerry Jinfeng Guo, P. Bauszat, Jacco Bikker, E. Eisemann
DOI: https://doi.org/10.2312/sre.20181174
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Computer Graphics and Visualisation

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.

Files

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