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dc.contributor.authorWang, Tongen_US
dc.contributor.authorSuda, Reijien_US
dc.contributor.editorVlastimil Havran and Karthik Vaiyanathanen_US
dc.date.accessioned2017-12-06T19:47:41Z
dc.date.available2017-12-06T19:47:41Z
dc.date.issued2017
dc.identifier.isbn978-1-4503-5101-0
dc.identifier.issn2079-8679
dc.identifier.urihttp://dx.doi.org/10.1145/3105762.3105778
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3105762-3105778
dc.description.abstractIt is generally accepted that Poisson disk sampling provides great properties in various applications in computer graphics. We present KD-tree based randomized tiling (KDRT), an e cient method to generate maximal Poisson-disk samples by replicating and conquering tiles clipped from a pa ern of very small size. Our method is a twostep process: rst, randomly clipping tiles from an MPS(Maximal Poisson-disk Sample) pa ern, and second, conquering these tiles together to form the whole sample plane. e results showed that this method can e ciently generate maximal Poisson-disk samples with very small trade-o in bias error. ere are two main contributions of this paper: First, a fast and robust Poisson-disk sample generation method is presented; Second, this method can be used to combine several groups of independently generated sample pa erns to form a larger one, thus can be applied as a general parallelization scheme of any MPS methods.en_US
dc.publisherACMen_US
dc.subjectComputing methodologies Computer graphics
dc.subjectPoisson
dc.subjectdisk Sampling
dc.titleFast Maximal Poisson-Disk Sampling by Randomized Tilingen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on High Performance Graphics
dc.description.sectionheadersReal-Time Graphics Techniques
dc.identifier.doi10.1145/3105762.3105778


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