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Extended cone-curvature based salient points detection and 3D model retrieval

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Extendedcone-curvaturebasedsalientpointsdetection

and3Dmodelretrieval

YuJieLiu&XiaoDongZhang&ZongMinLi&HuaLi

Publishedonline:3January2012#SpringerScience+BusinessMedia,LLC2011

AbstractLocalfeatureextractionof3Dmodelhasbecomeamoreandmoreimportant

aspectintermsof3Dmodelshapefeatureextraction.Comparedwiththeglobalfeature,itis

moresuitabletodothepartialretrievalandmorerobusttothemodeldeformation.Inthispaper,

alocalfeaturecalledextendedcone-curvaturefeatureisproposedtodescribethelocalshape

featureof3Dmodelmesh.Basedontheextendedcone-curvaturefeature,salientpointsand

salientregionsareextractedbyusinganewsalientpointdetectionmethod.Thenextended

cone-curvaturefeatureandlocalshapedistributionfeaturecalculatedonthesalientregionsare

usedtogetherasshapeindex,andtheearthmover’sdistanceisemployedtoaccomplish

similaritymeasure.Aftermanytimes’retrievalexperiments,thenewextendedcone-

curvaturedescriptorweproposehasmoreefficientandeffectiveperformancethanshape

distributiondescriptorandlightfielddescriptorespeciallyondeformablemodelretrieval.

Keywords3Dmodelretrieval.Extendedcone-curvature.Salientpoints.Earthmover’s

distanceMultimedToolsAppl(2013)64:671–693DOI10.1007/s11042-011-0950-7

ResearchHighlightsExtendsthecone-curvatureanddefinetheconceptofextendedcone-curvatureofgeneralmeshandgivesamethodtocomputeextendedcone-curvatureontriangularmeshes.Analgorithmisproposedtodetectthesalientpointsbasedonextendedcone-curvatureandsalientregionsareextractedaccordingtothesesalientpoints.Extendedcone-curvaturefeatureandshapedistributionfeatureofthesalientregionsareusedtogetherasthemodelindexwhichperformsverywellinthe3DmodelretrievalwithEarthMover’sDistanceasthesimilaritymeasure.

Y.Liu(*):Z.LiSchoolofComputerScienceandCommunicationEngineering,ChinaUniversityofPetroleum,Qingdao266555,Chinae-mail:bilin_2008@163.com

X.ZhangCenterforHumanComputerInteraction,ShenzhenInstituteofAdvancedIntegrationTechnology,Qingdao266555,China

H.LiNationalResearchCenterforIntelligentComputingSystems,InstituteofComputingTechnology,ChineseAcademyofSciences,Beijing100190,China1Introduction

Sincethefastdevelopmentof3Dtechnologiesandcomputergraphicsattheendofthe

twentiethcentury,3Dmodelhasbeenwidelyused,suchascomputergames,movies,

mechanicalCAD,virtualrealityandsoon.Atthesametime,theInternetprovidesanother

platformtofacilitateitswideapplicationTherefore,theamountof3Dmodelsbecomes

largerandlarger.3Dmodelhasbeenregardedasthefourth-generationmultimediainfor-

mationaftervoice,imageandvideo.

However,3Dmodelingwithahighprecisionisstilladifficultandtime-consuming

procedure.Ifwecanreusetheexistingmodels,itwillbringagreatimprovementofthe

modelingefficiency.Buthowcanwefindtheexactmodelsfrommillionsof3dmodels

withintheshortesttime?

Keyword-based3Dmodelretrievalhasbeenusedtoassistuserstofinddesiredmodels

initially.Everymodelin3Dmodeldatabaseislabeledbyoneormorekeywords.Although

thismethodhashighretrievalspeedandaccurateresults,twodisadvantagespreventitfrom

beingwidelyapplied.Firstly,it’saverytime-consumingandlaborioustasktolabelall3d

modelsifthedatabaseishuge.Secondly,thekeywordscanbeeasilyaffectedbyhumans’

subjectivethoughts.Differentpeoplemayhavedifferentconceptsforthesamemodel.So

thistraditionalmethodcannotmeetallusers’demands.

Thecontent-based3Dmodelretrievalhasbeenaresearchhotspotsince2000.It

breaksthroughtheconstraintsofthekeyword-basedmethodandmakesuseofthe

visualfeatureofthemodelsasthemodelindexdirectly.Atpresentthereareseveral

content-based3Dmodelretrievalsystemsforinstance,thePrincetonUniversity’s3D

modelretrievalsystem[6].Therearefivemainmodulesintheseretrievalsystems:the

userinterface,3Dmodeldatabase,featuredatabase,featureextractionandsimilarity

computation.ThestructureofthesystemisshowninFig.1.Atfirst,featuresofall

themodelsinthemodeldatabaseareextractedofflinebyusingfeatureextraction

algorithmandthensavedinthefeaturedatabase.Whenusersubmitsaquerymodel

throughtheuserinterface,thefeatureofthequerymodeliscalculatedsoon.Thenthe

similaritybetweenthequerymodelandallmodelsinthedatabasearecomputedby

thedistancebetweenthefeatureofthequerymodelandthefeaturesinthefeature

database.Afterthat,thedatabasemodelsarerankedbythedistance.Themodels

rankedinthemosttoparethemostsimilartothequerymodel.Theranklistis

returnedgraphicallytotheuserinterfacewheretheretrievalresultisdisplayed.

Featureextractionof3Dmodelsdefinitelyplaysaveryimportantroleintheretrieval

system.Althoughtherehavebeentremendousfeatureextractionmethods,noneofthemcan

befitforallsituations.Manyfeaturedescriptorsfromthesemethodsusuallyarenon-

topologicalfeaturesthatcanbeaffectedbyshapedeformationeasily.Sometimestopological

featuressuchasgraphorskeletonarehiredtosolvetheproblem,buttheyneedmore

computationandareverysensitivetomodelnoisewhichwillbeanalyzedintherelated

worksection.Inthispaperalocalshapefeatureextractionmethodcalledextendedcone-

curvatureisproposed.

Therestofthispaperisorganizedasfollows.Therelatedworkisdescribedin

section2.Insection3extendedcone-curvatureisdefinedandthealgorithmto

computeextendedcone-curvatureofeachtriangularmeshisalsoexplained.Section4

givesthealgorithmtodetectthesalientpointsandsalientregionsof3Dmodelsand

Section5introducestheearthmover’sdistancetocomputesimilaritybetweenthese

salientfeatures.Thentheexperimentalresultsareshowninthesection6.Finally,

conclusionsaregiveninsection7

.672MultimedToolsAppl(2013)64:671–693