Making Gabor Noise Fast and Normalized

dc.contributor.authorTavernier, Vincenten_US
dc.contributor.authorNeyret, Fabriceen_US
dc.contributor.authorVergne, Romainen_US
dc.contributor.authorThollot, Joëlleen_US
dc.contributor.editorCignoni, Paolo and Miguel, Ederen_US
dc.date.accessioned2019-05-05T17:49:49Z
dc.date.available2019-05-05T17:49:49Z
dc.date.issued2019
dc.description.abstractGabor Noise is a powerful procedural texture synthesis technique, but it has two major drawbacks: It is costly due to the high required splat density and not always predictable because properties of instances can differ from those of the process. We bench performance and quality using alternatives for each Gabor Noise ingredient: point distribution, kernel weighting and kernel shape. For this, we introduce 3 objective criteria to measure process convergence, process stationarity, and instance stationarity. We show that minor implementation changes allow for 17-24x speed-up with same or better quality.en_US
dc.description.sectionheadersImage and Video
dc.description.seriesinformationEurographics 2019 - Short Papers
dc.identifier.doi10.2312/egs.20191009
dc.identifier.issn1017-4656
dc.identifier.pages37-40
dc.identifier.urihttps://doi.org/10.2312/egs.20191009
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20191009
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectTexturing
dc.titleMaking Gabor Noise Fast and Normalizeden_US
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