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Applying Visual Analytics to Physically Based Rendering
Author(s) -
Simons G.,
Herholz S.,
Petitjean V.,
Rapp T.,
Ament M.,
Lensch H.,
Dachsbacher C.,
Eisemann M.,
Eisemann E.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13452
Subject(s) - computer science , rendering (computer graphics) , computer graphics (images) , visualization , parallel rendering , real time rendering , visual analytics , software rendering , interactivity , computer vision , histogram , 3d rendering , artificial intelligence , computer graphics , multimedia , 3d computer graphics , image (mathematics)
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. Two‐dimensional (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.