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Article: Differential domain analysis for non-uniform sampling

TitleDifferential domain analysis for non-uniform sampling
Authors
KeywordsAnalysis
Differential domain
Noise
Non-uniform
Sampling
Spectrum
Issue Date2011
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.org
Citation
ACM Transactions on Graphics, 2011, v. 30 n. 4, article no. 50, p. 50:1-50:10 How to Cite?
Abstract
Sampling is a core component for many graphics applications including rendering, imaging, animation, and geometry processing. The efficacy of these applications often crucially depends upon the distribution quality of the underlying samples. While uniform sampling can be analyzed by using existing spatial and spectral methods, these cannot be easily extended to general non-uniform settings, such as adaptive, anisotropic, or non-Euclidean domains. We present new methods for analyzing non-uniform sample distributions. Our key insight is that standard Fourier analysis, which depends on samples' spatial locations, can be reformulated into an equivalent form that depends only on the distribution of their location differentials. We call this differential domain analysis. The main benefit of this reformulation is that it bridges the fundamental connection between the samples' spatial statistics and their spectral properties. In addition, it allows us to generalize our method with different computation kernels and differential measurements. Using this analysis, we can quantitatively measure the spatial and spectral properties of various non-uniform sample distributions, including adaptive, anisotropic, and non-Euclidean domains. © 2011 ACM.
DescriptionProceedings of ACM SIGGRAPH '11 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, 7-11 August 2011
Persistent Identifierhttp://hdl.handle.net/10722/141807
ISSN
2013 Impact Factor: 3.725
ISI Accession Number ID
Funding AgencyGrant Number
NSFCCF-0746577
Funding Information:

We would like to thank Hongwei Li for clarifying details in [Li et al. 2010], and SIGGRAPH anonymous reviewers for their suggestions. This work is supported in part by NSF grant CCF-0746577.

References

 

Author Affiliations
  1. University of Massachusetts Amherst
  2. Microsoft Research
DC FieldValueLanguage
dc.contributor.authorWei, LYen_HK
dc.contributor.authorWang, Ren_HK
dc.date.accessioned2011-09-27T03:02:15Z-
dc.date.available2011-09-27T03:02:15Z-
dc.date.issued2011en_HK
dc.identifier.citationACM Transactions on Graphics, 2011, v. 30 n. 4, article no. 50, p. 50:1-50:10en_HK
dc.identifier.issn0730-0301en_HK
dc.identifier.urihttp://hdl.handle.net/10722/141807-
dc.descriptionProceedings of ACM SIGGRAPH '11 Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, 7-11 August 2011-
dc.description.abstractSampling is a core component for many graphics applications including rendering, imaging, animation, and geometry processing. The efficacy of these applications often crucially depends upon the distribution quality of the underlying samples. While uniform sampling can be analyzed by using existing spatial and spectral methods, these cannot be easily extended to general non-uniform settings, such as adaptive, anisotropic, or non-Euclidean domains. We present new methods for analyzing non-uniform sample distributions. Our key insight is that standard Fourier analysis, which depends on samples' spatial locations, can be reformulated into an equivalent form that depends only on the distribution of their location differentials. We call this differential domain analysis. The main benefit of this reformulation is that it bridges the fundamental connection between the samples' spatial statistics and their spectral properties. In addition, it allows us to generalize our method with different computation kernels and differential measurements. Using this analysis, we can quantitatively measure the spatial and spectral properties of various non-uniform sample distributions, including adaptive, anisotropic, and non-Euclidean domains. © 2011 ACM.en_HK
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tog.acm.orgen_US
dc.relation.ispartofACM Transactions on Graphicsen_HK
dc.subjectAnalysisen_HK
dc.subjectDifferential domainen_HK
dc.subjectNoiseen_HK
dc.subjectNon-uniformen_HK
dc.subjectSamplingen_HK
dc.subjectSpectrumen_HK
dc.titleDifferential domain analysis for non-uniform 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.doi10.1145/1964921.1964945en_HK
dc.identifier.scopuseid_2-s2.0-80051890935en_HK
dc.identifier.hkuros206832-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80051890935&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume30en_HK
dc.identifier.issue4en_HK
dc.identifier.eissn1557-7368-
dc.identifier.isiWOS:000297216400024-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridWei, LY=14523963300en_HK
dc.identifier.scopusauthoridWang, R=36072127500en_HK

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