Visually Comparing Rendering Performance from Multiple Perspectives

Faculty/Professorship: Information Visualisation 
Author(s): Tarner, Hagen; Bruder, Valentin; Frey, Steffen; Ertl, Thomas; Beck, Fabian  
Title of the compilation: Vision, Modeling, and Visualization
Editors: Bender, Jan; Botsch, Mario; Keim, Daniel A.
Conference: VMV 2022
Publisher Information: Eindhoven : The Eurographics Association
Year of publication: 2022
Pages: 115-125
ISBN: 978-3-03868-189-2
Language(s): English
DOI: 10.2312/VMV.20221211
Evaluation of rendering performance is crucial when selecting or developing algorithms, but challenging as performance can largely differ across a set of selected scenarios. Despite this, performance metrics are often reported and compared in a highly aggregated way. In this paper we suggest a more fine-grained approach for the evaluation of rendering performance, taking into account multiple perspectives on the scenario: camera position and orientation along different paths, rendering algorithms, image resolution, and hardware. The approach comprises a visual analysis system that shows and contrasts the data from these perspectives. The users can explore combinations of perspectives and gain insight into the performance characteristics of several rendering algorithms. A stylized representation of the camera path provides a base layout for arranging the multivariate performance data as radar charts, each comparing the same set of rendering algorithms while linking the performance data with the rendered images. To showcase our approach, we analyze two types of scientific visualization benchmarks.
GND Keywords: Rendering; Leistungsbewertung; Algorithmentheorie; Vision, Modeling and Visualization
Keywords: performance visualization, visual comparison
DDC Classification: 004 Computer science  
RVK Classification: ST 320   
Peer Reviewed: Ja
International Distribution: Ja
Open Access Journal: Ja
Type: Conferenceobject
Release Date: 14. December 2022