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Article: Parallel Poisson disk sampling

TitleParallel Poisson disk sampling
Authors
KeywordsBlue noise
GPU techniques
Parallel computation
Poisson disk
Sampling
Texture synthesis
Issue Date2008
PublisherAssociation for Computing Machinery, Inc
Citation
Acm Transactions On Graphics, 2008, v. 27 n. 3 How to Cite?
AbstractSampling is important for a variety of graphics applications include rendering, imaging, and geometry processing. However, producing sample sets with desired efficiency and blue noise statistics has been a major challenge, as existing methods are either sequential with limited speed, or are parallel but only through pre-computed datasets and thus fall short in producing samples with blue noise statistics. We present a Poisson disk sampling algorithm that runs in parallel and produces all samples on the fly with desired blue noise properties. Our main idea is to subdivide the sample domain into grid cells and we draw samples concurrently from multiple cells that are sufficiently far apart so that their samples cannot conflict one another. We present a parallel implementation of our algorithm running on a GPU with constant cost per sample and constant number of computation passes for a target number of samples. Our algorithm also works in arbitrary dimension, and allows adaptive sampling from a user-specified importance field. Furthermore, our algorithm is simple and easy to implement, and runs faster than existing techniques. © 2008 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/141795
ISSN
2015 Impact Factor: 4.218
2015 SCImago Journal Rankings: 2.552
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWei, LYen_HK
dc.date.accessioned2011-09-27T03:02:02Z-
dc.date.available2011-09-27T03:02:02Z-
dc.date.issued2008en_HK
dc.identifier.citationAcm Transactions On Graphics, 2008, v. 27 n. 3en_HK
dc.identifier.issn0730-0301en_HK
dc.identifier.urihttp://hdl.handle.net/10722/141795-
dc.description.abstractSampling is important for a variety of graphics applications include rendering, imaging, and geometry processing. However, producing sample sets with desired efficiency and blue noise statistics has been a major challenge, as existing methods are either sequential with limited speed, or are parallel but only through pre-computed datasets and thus fall short in producing samples with blue noise statistics. We present a Poisson disk sampling algorithm that runs in parallel and produces all samples on the fly with desired blue noise properties. Our main idea is to subdivide the sample domain into grid cells and we draw samples concurrently from multiple cells that are sufficiently far apart so that their samples cannot conflict one another. We present a parallel implementation of our algorithm running on a GPU with constant cost per sample and constant number of computation passes for a target number of samples. Our algorithm also works in arbitrary dimension, and allows adaptive sampling from a user-specified importance field. Furthermore, our algorithm is simple and easy to implement, and runs faster than existing techniques. © 2008 ACM.en_HK
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Incen_US
dc.relation.ispartofACM Transactions on Graphicsen_HK
dc.subjectBlue noiseen_HK
dc.subjectGPU techniquesen_HK
dc.subjectParallel computationen_HK
dc.subjectPoisson disken_HK
dc.subjectSamplingen_HK
dc.subjectTexture synthesisen_HK
dc.titleParallel Poisson disk samplingen_HK
dc.typeArticleen_HK
dc.identifier.emailWei, LY:lywei@cs.hku.hken_HK
dc.identifier.authorityWei, LY=rp01528en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1145/1360612.1360619en_HK
dc.identifier.scopuseid_2-s2.0-49249098277en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-49249098277&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume27en_HK
dc.identifier.issue3en_HK
dc.identifier.eissn1557-7368-
dc.identifier.isiWOS:000258262000009-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWei, LY=14523963300en_HK

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