PixelPie: Maximal Poisson-disk Sampling with Rasterization

dc.contributor.authorIp, Cheuk Yiuen_US
dc.contributor.authorYalc, M. Adilen_US
dc.contributor.authorLuebke, Daviden_US
dc.contributor.authorVarshney, Amitabhen_US
dc.contributor.editorKayvon Fatahalian and Christian Theobalten_US
dc.date.accessioned2016-02-18T11:23:01Z
dc.date.available2016-02-18T11:23:01Z
dc.date.issued2013en_US
dc.description.abstractWe present PixelPie, a highly parallel geometric formulation of the Poisson-disk sampling problem on the graphics pipeline. Traditionally, generating a distribution by throwing darts and removing conflicts has been viewed as an inherently sequential process. In this paper, we present an efficient Poisson-disk sampling algorithm that uses rasterization in a highly parallel manner. Our technique is an iterative two step process. The first step of each iteration involves rasterization of random darts at varying depths. The second step involves culling conflicted darts. Successive iterations identify and fill in the empty regions to obtain maximal distributions. Our approach maps well to the parallel and optimized graphics functions on the GPU and can be easily extended to perform importance sampling. Our implementation can generate Poisson-disk samples at the rate of nearly 7 million samples per second on a GeForce GTX 580 and is significantly faster than the state-of-the-art maximal Poisson-disk sampling techniques.en_US
dc.description.sectionheadersAdvanced Rasterizationen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on High Performance Graphicsen_US
dc.identifier.doi10.1145/2492045.2492047en_US
dc.identifier.isbn978-1-4503-2135-8en_US
dc.identifier.issn2079-8687en_US
dc.identifier.pages17-26en_US
dc.identifier.urihttps://doi.org/10.1145/2492045.2492047en_US
dc.publisherACMen_US
dc.subjectCR Categoriesen_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectAntialiasingen_US
dc.subjectI.4.1 [Computer Graphics]en_US
dc.subjectImage Processing and Computer Visionen_US
dc.subjectDigitization and Image Captureen_US
dc.subjectSamplingen_US
dc.subjectKeywordsen_US
dc.subjectPoissonen_US
dc.subjectdisk samplingen_US
dc.subjectGPGPUen_US
dc.subjectdart throwingen_US
dc.subjectmaximal samplingen_US
dc.titlePixelPie: Maximal Poisson-disk Sampling with Rasterizationen_US
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