Show simple item record

dc.contributor.authorBruder, Valentinen_US
dc.contributor.authorSchulz, Christophen_US
dc.contributor.authorBauer, Rubenen_US
dc.contributor.authorFrey, Steffenen_US
dc.contributor.authorWeiskopf, Danielen_US
dc.contributor.authorErtl, Thomasen_US
dc.contributor.editorJohansson, Jimmy and Sadlo, Filip and Marai, G. Elisabetaen_US
dc.date.accessioned2019-06-02T18:14:41Z
dc.date.available2019-06-02T18:14:41Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-090-1
dc.identifier.urihttps://doi.org/10.2312/evs.20191172
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20191172
dc.description.abstractFoveal vision is located in the center of the field of view with a rich impression of detail and color, whereas peripheral vision occurs on the side with more fuzzy and colorless perception. This visual acuity fall-off can be used to achieve higher frame rates by adapting rendering quality to the human visual system. Volume raycasting has unique characteristics, preventing a direct transfer of many traditional foveated rendering techniques. We present an approach that utilizes the visual acuity fall-off to accelerate volume rendering based on Linde-Buzo-Gray sampling and natural neighbor interpolation. First, we measure gaze using a stationary 1200 Hz eye-tracking system. Then, we adapt our sampling and reconstruction strategy to that gaze. Finally, we apply a temporal smoothing filter to attenuate undersampling artifacts since peripheral vision is particularly sensitive to contrast changes and movement. Our approach substantially improves rendering performance with barely perceptible changes in visual quality. We demonstrate the usefulness of our approach through performance measurements on various data sets.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectScientific visualization
dc.subjectComputing methodologies
dc.subjectPerception
dc.titleVoronoi-Based Foveated Volume Renderingen_US
dc.description.seriesinformationEuroVis 2019 - Short Papers
dc.description.sectionheadersVolume, Simulation, and Data Reduction
dc.identifier.doi10.2312/evs.20191172
dc.identifier.pages67-71


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record