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Conference Paper: Parallel poisson disk sampling with spectrum analysis on surfaces
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TitleParallel poisson disk sampling with spectrum analysis on surfaces
 
AuthorsBowers, J1
Wang, R1
Wei, LY2
Maletz, D1
 
KeywordsGPU
manifold surface
mesh Laplacian
parallel computation
Poisson disk sampling
spectrum analysis
 
Issue Date2010
 
PublisherAssociation for Computing Machinery, Inc
 
CitationAcm Transactions On Graphics, 2010, v. 29 n. 6 [How to Cite?]
DOI: http://dx.doi.org/10.1145/1866158.1866188
 
AbstractThe ability to place surface samples with Poisson disk distribution can benefit a variety of graphics applications. Such a distribution satisfies the blue noise property, i.e. lack of low frequency noise and structural bias in the Fourier power spectrum. While many techniques are available for sampling the plane, challenges remain for sampling arbitrary surfaces. In this paper, we present new methods for Poisson disk sampling with spectrum analysis on arbitrary manifold surfaces. Our first contribution is a parallel dart throwing algorithm that generates high-quality surface samples at interactive rates. It is flexible and can be extended to adaptive sampling given a user-specified radius field. Our second contribution is a new method for analyzing the spectral quality of surface samples. Using the spectral mesh basis derived from the discrete mesh Laplacian operator, we extend standard concepts in power spectrum analysis such as radial means and anisotropy to arbitrary manifold surfaces. This provides a way to directly evaluate the spectral distribution quality of surface samples without requiring mesh parameterization. Finally, we implement our Poisson disk sampling algorithm on the GPU, and demonstrate practical applications involving interactive sampling and texturing on arbitrary surfaces. © 2010 ACM.
 
ISSN0730-0301
2012 Impact Factor: 3.361
2012 SCImago Journal Rankings: 3.315
 
DOIhttp://dx.doi.org/10.1145/1866158.1866188
 
ISI Accession Number IDWOS:000284943000030
Funding AgencyGrant Number
NSFCCF-0746577
Funding Information:

The authors would like to thank SIGGRAPH Asia anonymous reviewers for their feedback and comments. Rui Wang is supported in part by NSF grant CCF-0746577. John Bowers is supported by an NSF graduate research fellowship.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorBowers, J
 
dc.contributor.authorWang, R
 
dc.contributor.authorWei, LY
 
dc.contributor.authorMaletz, D
 
dc.date.accessioned2011-09-27T03:01:39Z
 
dc.date.available2011-09-27T03:01:39Z
 
dc.date.issued2010
 
dc.description.abstractThe ability to place surface samples with Poisson disk distribution can benefit a variety of graphics applications. Such a distribution satisfies the blue noise property, i.e. lack of low frequency noise and structural bias in the Fourier power spectrum. While many techniques are available for sampling the plane, challenges remain for sampling arbitrary surfaces. In this paper, we present new methods for Poisson disk sampling with spectrum analysis on arbitrary manifold surfaces. Our first contribution is a parallel dart throwing algorithm that generates high-quality surface samples at interactive rates. It is flexible and can be extended to adaptive sampling given a user-specified radius field. Our second contribution is a new method for analyzing the spectral quality of surface samples. Using the spectral mesh basis derived from the discrete mesh Laplacian operator, we extend standard concepts in power spectrum analysis such as radial means and anisotropy to arbitrary manifold surfaces. This provides a way to directly evaluate the spectral distribution quality of surface samples without requiring mesh parameterization. Finally, we implement our Poisson disk sampling algorithm on the GPU, and demonstrate practical applications involving interactive sampling and texturing on arbitrary surfaces. © 2010 ACM.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationAcm Transactions On Graphics, 2010, v. 29 n. 6 [How to Cite?]
DOI: http://dx.doi.org/10.1145/1866158.1866188
 
dc.identifier.doihttp://dx.doi.org/10.1145/1866158.1866188
 
dc.identifier.eissn1557-7368
 
dc.identifier.isiWOS:000284943000030
Funding AgencyGrant Number
NSFCCF-0746577
Funding Information:

The authors would like to thank SIGGRAPH Asia anonymous reviewers for their feedback and comments. Rui Wang is supported in part by NSF grant CCF-0746577. John Bowers is supported by an NSF graduate research fellowship.

 
dc.identifier.issn0730-0301
2012 Impact Factor: 3.361
2012 SCImago Journal Rankings: 3.315
 
dc.identifier.issue6
 
dc.identifier.scopuseid_2-s2.0-78650876037
 
dc.identifier.urihttp://hdl.handle.net/10722/141786
 
dc.identifier.volume29
 
dc.languageeng
 
dc.publisherAssociation for Computing Machinery, Inc
 
dc.publisher.placeUnited States
 
dc.relation.ispartofACM Transactions on Graphics
 
dc.relation.referencesReferences in Scopus
 
dc.subjectGPU
 
dc.subjectmanifold surface
 
dc.subjectmesh Laplacian
 
dc.subjectparallel computation
 
dc.subjectPoisson disk sampling
 
dc.subjectspectrum analysis
 
dc.titleParallel poisson disk sampling with spectrum analysis on surfaces
 
dc.typeConference_Paper
 
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Author Affiliations
  1. University of Massachusetts Amherst
  2. Microsoft Research