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Article: Recovering geometric detail by octree normal maps

TitleRecovering geometric detail by octree normal maps
Authors
KeywordsGpu
Large Scale Models
Octree Textures
Recovering Geometric Detail
Issue Date2012
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2012, v. 7145 LNCS, p. 62-73 How to Cite?
AbstractThis paper presents a new approach for constructing normal maps that capture high-frequency geometric detail from dense models of arbitrary topology and are applied to the simplified version of the same models generated by any simplification method to mimic the same level of detail. A variant of loose octree scheme is used to optimally calculate the mesh normals. A B-spline surface fitting based method is employed to solve the issue of thin plate. A memory saving Breadth-First Search (BFS) order construction is designed. Furthermore, a speedup scheme that exploits access coherence is used to accelerate filtering operation. The proposed method can synthesize good quality images of models with extremely high number of polygons while using much less memory and render at much higher frame rate. © 2012 Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/152500
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorFan, Wen_US
dc.contributor.authorWang, Ben_US
dc.contributor.authorChan, Ben_US
dc.contributor.authorPaul, JCen_US
dc.contributor.authorSun, Jen_US
dc.date.accessioned2012-06-26T06:39:43Z-
dc.date.available2012-06-26T06:39:43Z-
dc.date.issued2012en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2012, v. 7145 LNCS, p. 62-73en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/152500-
dc.description.abstractThis paper presents a new approach for constructing normal maps that capture high-frequency geometric detail from dense models of arbitrary topology and are applied to the simplified version of the same models generated by any simplification method to mimic the same level of detail. A variant of loose octree scheme is used to optimally calculate the mesh normals. A B-spline surface fitting based method is employed to solve the issue of thin plate. A memory saving Breadth-First Search (BFS) order construction is designed. Furthermore, a speedup scheme that exploits access coherence is used to accelerate filtering operation. The proposed method can synthesize good quality images of models with extremely high number of polygons while using much less memory and render at much higher frame rate. © 2012 Springer-Verlag Berlin Heidelberg.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.subjectGpuen_US
dc.subjectLarge Scale Modelsen_US
dc.subjectOctree Texturesen_US
dc.subjectRecovering Geometric Detailen_US
dc.titleRecovering geometric detail by octree normal mapsen_US
dc.typeArticleen_US
dc.identifier.emailChan, B:bchan@cs.hku.hken_US
dc.identifier.authorityChan, B=rp00086en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/978-3-642-29050-3_6en_US
dc.identifier.scopuseid_2-s2.0-84860140953en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84860140953&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume7145 LNCSen_US
dc.identifier.spage62en_US
dc.identifier.epage73en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridFan, W=54414133800en_US
dc.identifier.scopusauthoridWang, B=55195034100en_US
dc.identifier.scopusauthoridChan, B=26662908500en_US
dc.identifier.scopusauthoridPaul, JC=35578161900en_US
dc.identifier.scopusauthoridSun, J=34873504900en_US

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