Applying Visual Analytics to Physically Based Rendering

Journal Article (2018)
Author(s)

Gerard Simons (External organisation)

Sebastian Herholz (Eberhard Karls Universität Tübingen)

Victor Petitjean (TU Delft - Computer Graphics and Visualisation)

Tobias Rapp (Karlsruhe Institut für Technologie)

Marco Ament (Karlsruhe Institut für Technologie)

Hendrick Lensch (Eberhard Karls Universität Tübingen)

Carsten Dachsbacher (Karlsruhe Institut für Technologie)

Martin Eisemann (Technische Hochschule Köln)

Elmar Eisemann (TU Delft - Computer Graphics and Visualisation)

DOI related publication
https://doi.org/10.1111/cgf.13452 Final published version
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Publication Year
2018
Language
English
Journal title
Computer Graphics Forum (online)
Pages (from-to)
1-12
Downloads counter
253
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Abstract

Physically based rendering is a well-understood technique to produce realistic-looking images. However, different algorithms exist for efficiency reasons, which work well in certain cases but fail or produce rendering artefacts in others. Few tools allow a user to gain insight into the algorithmic processes. In this work, we present such a tool, which combines techniques from information visualization and visual analytics with physically based rendering. It consists of an interactive parallel coordinates plot, with a built-in sampling-based data reduction technique to visualize the attributes associated with each light sample. Twodimensional (2D) and three-dimensional (3D) heat maps depict any desired property of the rendering process. An interactively rendered 3D view of the scene displays animated light paths based on the user’s selection to gain further insight into the rendering process. The provided interactivity enables the user to guide the rendering process for more efficiency. To show its usefulness, we present several applications based on our tool. This includes differential light transport visualization to optimize light setup in a scene, finding the causes of and resolving rendering artefacts, such as fireflies, as well as a path length contribution histogram to evaluate the efficiency of different Monte Carlo estimators.

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