File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Denoising point sets via L0 minimization

TitleDenoising point sets via L0 minimization
Authors
KeywordsPoint set
Denoising
L0 minimization
L0 sparsity
Issue Date2015
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/cagd
Citation
Computer Aided Geometric Design, 2015, v. 35-36, p. 2-15 How to Cite?
AbstractWe present an anisotropic point cloud denoising method using L0 minimization. The L0 norm directly measures the sparsity of a solution, and we observe that many common objects can be defined as piecewise smooth surfaces with a small number of features. Hence, we demonstrate how to apply an L0 optimization directly to point clouds, which produces sparser solutions and sharper surfaces than either the L1 or L2 norm. Our method can faithfully recover sharp features while at the same time smoothing the remaining regions even in the presence of large amounts of noise.
Persistent Identifierhttp://hdl.handle.net/10722/220471
ISSN
2021 Impact Factor: 1.368
2020 SCImago Journal Rankings: 0.416
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSun, Y-
dc.contributor.authorSchaefer, S-
dc.contributor.authorWang, W-
dc.date.accessioned2015-10-16T06:43:19Z-
dc.date.available2015-10-16T06:43:19Z-
dc.date.issued2015-
dc.identifier.citationComputer Aided Geometric Design, 2015, v. 35-36, p. 2-15-
dc.identifier.issn0167-8396-
dc.identifier.urihttp://hdl.handle.net/10722/220471-
dc.description.abstractWe present an anisotropic point cloud denoising method using L0 minimization. The L0 norm directly measures the sparsity of a solution, and we observe that many common objects can be defined as piecewise smooth surfaces with a small number of features. Hence, we demonstrate how to apply an L0 optimization directly to point clouds, which produces sparser solutions and sharper surfaces than either the L1 or L2 norm. Our method can faithfully recover sharp features while at the same time smoothing the remaining regions even in the presence of large amounts of noise.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/cagd-
dc.relation.ispartofComputer Aided Geometric Design-
dc.subjectPoint set-
dc.subjectDenoising-
dc.subjectL0 minimization-
dc.subjectL0 sparsity-
dc.titleDenoising point sets via L0 minimization-
dc.typeArticle-
dc.identifier.emailSun, Y: yujing@hku.hk-
dc.identifier.emailWang, W: wenping@cs.hku.hk-
dc.identifier.authoritySun, Y=rp02880-
dc.identifier.authorityWang, W=rp00186-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.cagd.2015.03.011-
dc.identifier.scopuseid_2-s2.0-84941421545-
dc.identifier.hkuros256020-
dc.identifier.volume35-36-
dc.identifier.spage2-
dc.identifier.epage15-
dc.identifier.isiWOS:000356194100002-
dc.publisher.placeNetherlands-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats