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.
%0 Conference Paper
%1 tarner:2022:visually
%A Tarner, H.
%A Bruder, V.
%A Ertl, T.
%A Frey, S.
%A Beck, F.
%B Vision, Modeling, and Visualization
%D 2022
%E Bender, Jan
%E Botsch, Mario
%E Keim, Daniel
%I The Eurographics Association
%K a02 sfbtrr161 from:christinawarren 2022 visus:ertl visus
%R 10.2312/vmv.20221211
%T Visually Comparing Rendering Performance from Multiple Perspectives
%U https://doi.org/10.2312/vmv.20221211
%X 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.
@inproceedings{tarner:2022:visually,
abstract = {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.},
added-at = {2022-12-04T11:57:01.000+0100},
author = {Tarner, H. and Bruder, V. and Ertl, T. and Frey, S. and Beck, F.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e7aa940a7d07491b0f1a58964244ed27/visus},
booktitle = {Vision, Modeling, and Visualization},
doi = {10.2312/vmv.20221211},
editor = {Bender, Jan and Botsch, Mario and Keim, Daniel},
interhash = {7de6c2133a753ca28ec9a7031210a0e5},
intrahash = {e7aa940a7d07491b0f1a58964244ed27},
keywords = {a02 sfbtrr161 from:christinawarren 2022 visus:ertl visus},
publisher = {The Eurographics Association},
timestamp = {2022-12-04T10:57:01.000+0100},
title = {Visually Comparing Rendering Performance from Multiple Perspectives},
url = {https://doi.org/10.2312/vmv.20221211},
year = 2022
}
