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Article: Robust feature-preserving mesh denoising based on consistent subneighborhoods

TitleRobust feature-preserving mesh denoising based on consistent subneighborhoods
Authors
KeywordsBilateral Filtering.
Clustering
Curvature Tensors
Denoising
Features
Normals
Quadrics
Shared Nearest Neighbors
Issue Date2010
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tvcg
Citation
Ieee Transactions On Visualization And Computer Graphics, 2010, v. 16 n. 2, p. 312-324 How to Cite?
AbstractIn this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/152426
ISSN
2015 Impact Factor: 1.4
2015 SCImago Journal Rankings: 0.917
ISI Accession Number ID
Funding AgencyGrant Number
National Natural Science Foundation of China60728204/F020404
Funding Information:

The authors would like to thank the reviewers for their valuable comments. This work was partially supported by National Natural Science Foundation of China (60728204/F020404).

References

 

DC FieldValueLanguage
dc.contributor.authorFan, Hen_US
dc.contributor.authorYu, Yen_US
dc.contributor.authorPeng, Qen_US
dc.date.accessioned2012-06-26T06:38:24Z-
dc.date.available2012-06-26T06:38:24Z-
dc.date.issued2010en_US
dc.identifier.citationIeee Transactions On Visualization And Computer Graphics, 2010, v. 16 n. 2, p. 312-324en_US
dc.identifier.issn1077-2626en_US
dc.identifier.urihttp://hdl.handle.net/10722/152426-
dc.description.abstractIn this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved. © 2010 IEEE.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tvcgen_US
dc.relation.ispartofIEEE Transactions on Visualization and Computer Graphicsen_US
dc.subjectBilateral Filtering.en_US
dc.subjectClusteringen_US
dc.subjectCurvature Tensorsen_US
dc.subjectDenoisingen_US
dc.subjectFeaturesen_US
dc.subjectNormalsen_US
dc.subjectQuadricsen_US
dc.subjectShared Nearest Neighborsen_US
dc.titleRobust feature-preserving mesh denoising based on consistent subneighborhoodsen_US
dc.typeArticleen_US
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/TVCG.2009.70en_US
dc.identifier.scopuseid_2-s2.0-76849088927en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-76849088927&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume16en_US
dc.identifier.issue2en_US
dc.identifier.spage312en_US
dc.identifier.epage324en_US
dc.identifier.isiWOS:000273396600012-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridFan, H=35214600100en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US
dc.identifier.scopusauthoridPeng, Q=7202852366en_US

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