基于單幅圖像的三維對(duì)稱自由形體重建

基于單幅圖像的三維對(duì)稱自由形體重建Chapter1:Introduction

-Backgroundinformationontheimportanceof3Dshapereconstruction

-Briefoverviewofthemethodscurrentlyavailable

-Researchobjectivesandsignificance

Chapter2:LiteratureReview

-Areviewoftheliteratureon3Dshapereconstructionbasedonasingleimage

-Analysisofdifferentapproachesandtheiradvantagesandlimitations

-Discussionofexistingalgorithmsfor3Dshapereconstructionoffree-formobjects

Chapter3:Methodology

-Descriptionoftheproposedmethodfor3Dshapereconstruction

-Discussionofthekeystepsofthealgorithm

-Technicaldetailsoftheimplementation

Chapter4:ExperimentalResultsandAnalysis

-Evaluationoftheproposedmethodusingarealdataset

-Quantitativeanalysisoftheresultsandcomparisonwithexistingmethods

-Discussionofthefactorsinfluencingthequalityofthereconstructed3Dshape

Chapter5:ConclusionandFutureWork

-Summaryoftheproposedmethodanditsadvantagesoverexistingmethods

-Futureworktoimprovetheaccuracyandefficiencyoftheproposedmethod

-Conclusiononthesignificanceoftheresearchandpotentialforfutureapplications1.Introduction

Three-dimensional(3D)shapereconstructionisafundamentaltaskincomputervisionandgraphics,whichaimstorecoverthe3Dstructureofobjectsfrom2Dimagesorvideosequences.Ithasavarietyofapplicationssuchasvirtualreality,robotics,medicalimaging,digitalentertainment,andculturalheritagepreservation.Many3Dshapereconstructiontechniqueshavebeenproposedintheliterature,includingstereovision,structurefrommotion,photometricstereo,andshape-from-shading.However,theseapproacheshavesomelimitationssuchasrequiringmultipleimages,restrictedtospecificsurfaceproperties,orsufferingfromtheambiguityofthesolution.

Recently,significantprogresshasbeenmadein3Dshapereconstructionfromasingleimage,whichismorepracticalandcost-effectiveformanyapplications.Thebasicideaof3Dshapereconstructionfromasingleimageistoestimatethedepthmapofeachpixelintheimageandthenextrudea3Dsurfacefromit.Thedepthmapcanbeinferredfromvariouscuessuchastexture,shading,edges,symmetry,regularity,andgeometricpriors.Althoughsomeofthesecuesareambiguousorunreliable,thecombinationofthemcanleadtoamorerobustandaccuratereconstruction.

Theobjectiveofthisresearchistodevelopanovelmethodfor3Dshapereconstructionfromasingleimage,whichcanachievehighqualityandefficiency.Theproposedmethodemploysadeepneuralnetworktolearnthemappingfromtheimagetothe3Dshape,whichcancapturethecomplexandnon-linearrelationshipbetweenthem.Thenetworkarchitectureisdesignedbasedontheencoder-decoderparadigm,whichconsistsofaconvolutionalneuralnetwork(CNN)astheencoderandagenerativeadversarialnetwork(GAN)asthedecoder.TheCNNcanextractthehigh-levelfeaturesoftheimageandfeedthemtotheGANtogeneratethe3Dshapefromarandomnoise.

Thesignificanceofthisresearchliesintheimportanceof3Dshapereconstructioninmanypracticalapplications,thelimitationsofexistingmethods,andthepotentialofdeeplearninginaddressingtheseissues.Theproposedmethodcancontributetotheadvancementofthestate-of-the-artin3Dshapereconstructionandopenupnewpossibilitiesforvariousfields.Therestofthethesisisorganizedasfollows.Chapter2reviewstherelatedliteratureon3Dshapereconstructionbasedonasingleimage.Chapter3describestheproposedmethodindetail.Chapter4presentstheexperimentalresultsandanalysis.Chapter5concludesthethesisanddiscussesfuturework.Chapter2:LiteratureReview

Inrecentyears,3Dshapereconstructionfromasingleimagehasreceivedsignificantattentioninthecomputervisionandgraphicscommunity.Varioustechniqueshavebeenproposedtoaddressthisproblem,andwebrieflyreviewsomeofthemostrelevantliteratureinthefollowingsections.

2.1Geometry-basedmethods

Geometry-basedmethodsfor3DshapereconstructionfromasingleimageusuallyrelyontheassumptionofaLambertianorpiecewise-Lambertiansurfacemodel,whichassumesthatthesurfacehasdiffusereflectanceandislocallyflat.Popularapproachesinthiscategoryincludeshape-from-shading,photometricstereo,andshape-from-silhouette.

Shape-from-shading(SFS)estimatesthedepthmapoftheobjectbyanalyzingthevariationsofintensityonthesurfaceunderdifferentlightsources.However,SFSissensitivetothesurfacenormalsandlightingconditionsandcansufferfromtheambiguityofthesolution.

Photometricstereo(PS)similarlyusesmultipleimagestakenunderdifferentlightingconditionstoestimatethesurfacenormalsandthusthedepthmap.PScanhandlenon-Lambertiansurfaces,butrequiresatleastthreeimagesandcanbeaffectedbythenoiseandnon-uniformityofthelighting.

Shape-from-silhouette(SFS)isamethodthatextrudesthe3Dsurfacefromthecontoursoftheobjectintheimage.SFSassumesthatthesurfaceisconcaveandisoccludedfromviewbytheforeground,whichisoftenanunrealisticassumption.

2.2Learning-basedmethods

Learning-basedmethodshaveemergedasapromisingalternativetothegeometry-basedmethods,astheycancapturethecomplexandnon-linearrelationshipbetweentheimageandthe3Dshape.Popularapproachesinthiscategoryinclude3D-R2N2,VRN,andPixel2Mesh.

3D-R2N2isamethodthatutilizesarecurrentneuralnetwork(RNN)togenerateavoxelrepresentationofthe3Dshapefromasetofrendered2Dimages.Themethodcanhandlelarge-scaleshapesandcanproducedetailedgeometry,butrequiresalargeamountoftrainingdataandiscomputationallyexpensive.

Volumetricencoder-decodernetworks(VRN)useCNNstodirectlypredictavoxelrepresentationofthe3Dshapefromasingleimage.VRNcanproducehigh-qualityresultsandiscomputationallyefficient,butcansufferfromvoxelizationartifactsandrequiresafixedresolution.

Pixel2Meshisamethodthatgeneratesameshrepresentationofthe3Dshapefroma2DimagebypredictingtheverticesandedgesofthemeshusingaCNN.Pixel2Meshcangeneratewatertightmeshesandhandleoccludedsurfaces,butcanproduceinaccuratemeshesandhavedifficultywithsymmetricalshapes.

2.3Adversariallearningmethods

Adversariallearningmethodshaverecentlygainedpopularityfor3Dshapereconstruction,astheycangeneratehighlyrealisticanddetail-rich3Dshapes.Themostfamousmethodis3D-GANthatlearnstogenerate3Dshapesbyoptimizingadversarialloss.AnotherrelatedmethodisGAN-3DF,whichgenerates3Dshapesbylearningthemappingfromlatentvectorsto3Dshapes.

2.4Limitationsandchallenges

Despitethesignificantprogressin3Dshapereconstructionfromasingleimage,therearestillmanychallengesandlimitationstobeaddressed.Someofthemostpressingissuesincludetheneedforlargeamountsoftrainingdata,thetrade-offbetweenqualityandefficiency,thehandlingofocclusionandsymmetries,andtherobustnesstovariationsinlighting,texture,andshapecomplexity.

Insummary,3Dshapereconstructionfromasingleimageisanactiveandimportantresearchtopicwithmanypotentialapplications.Geometry-basedmethodsandlearning-basedmethodshavebothbeenproposedandhavetheirownstrengthsandlimitations.Adversariallearningmethodshaverecentlygainedpopularityfortheirabilitytogeneratehighlyrealisticanddetail-rich3Dshapes.Thechallengesandlimitationsofexistingmethodssuggestthepotentialfornewapproachesbasedondeeplearning,andweproposesuchanapproachinthenextchapter.Chapter3:ProposedMethod

Inthischapter,weproposeanoveldeeplearning-basedapproachfor3Dshapereconstructionfromasingleimagethatcombinesthestrengthsofbothgeometry-basedandlearning-basedmethods.Ourproposedmethodconsistsoftwomaincomponents:ageometry-basedmoduleandalearning-basedmodule.

3.1Geometry-basedModule

Thegeometry-basedmoduleutilizestheshape-from-shading(SFS)methodtoestimatethesurfacenormalsoftheobjectfromasingleimage.Thesurfacenormalsarethenusedtocomputethedepthmapoftheobjectandextractthesilhouetteoftheobject.Thedepthmapandsilhouettearethenpassedtothelearning-basedmoduleforfurtherprocessing.

3.2Learning-basedModule

Thelearning-basedmoduleisbasedonvolumetricencoder-decodernetworks(VRN)andtakesthedepthmapandsilhouettegeneratedbythegeometry-basedmoduleasinput.TheVRNnetworkistrainedtopredictthe3Dshapeoftheobjectasavolumetricrepresentation.

Thenetworkconsistsofthreemainlayers:anencoderthatprocessestheinputdataandencodesitintoalower-dimensionalrepresentation;adecoderthatprocessestheencodeddataandreconstructstheoutput;andadiscriminatorthatdistinguishesbetweenthereconstructedoutputandthegroundtruth.

Duringtraining,thenetworkisoptimizedtominimizethedifferencebetweenthereconstructedoutputandthegroundtruth,aswellastomaximizetheadversariallosscomputedbythediscriminator.Theadversariallossencouragesthenetworktogeneraterealisticandaccurate3Dshapesthatcloselymatchthegroundtruth.

3.3Integration

Theoutputofthelearning-basedmoduleisavolumetricrepresentationofthe3Dshapethatcanbevisualizedasameshorpointcloud.Toobtainamoreaccurateandvisuallyappealingrepresentation,weproposetointegratetheoutputofthelearning-basedmodulewiththeoutputofthegeometry-basedmodule.

Specifically,weemployasurfacereconstructionalgorithmtoextractameshsurfacefromthevolumetricrepresentationgeneratedbythelearning-basedmodule.Wethenuseasurfacerefinementalgorithmtosmoothandrefinethemeshsurface,whilepreservingthegeometricdetailsoftheoriginalshape.

Thefinaloutputofourproposedmethodisahigh-qualityandvisuallyappealing3Dshapethataccuratelycapturesthegeometryandappearanceoftheobjectfromasingleimage.

3.4AdvantagesandLimitations

Ourproposedmethodhasseveraladvantagesoverexistingmethods.Firstly,itcombinesthestrengthsofbothgeometry-basedandlearning-basedmethods,providingamorerobustandaccurateapproachfor3Dshapereconstruction.Secondly,itcanhandleocclusionandsymmetrieswithouttheneedforadditionalassumptionsordata.Finally,itcangeneratehigh-qualityandvisuallyappealing3Dshapesthatcloselymatchthegroundtruth.

However,ourproposedmethodalsohassomelimitations.Firstly,likealllearning-basedmethods,itrequiresalargeamountoftrainingdatatoachievegoodperformance.Secondly,itcanbecomputationallyexpensive,particularlyduringtraining.Finally,itmaystillsufferfromlimitationsinhandlingcomplexlightingconditionsandtextures.

Insummary,ourproposedmethodfor3Dshapereconstructionfromasingleimagecombinesthestrengthsofbothgeometry-basedandlearning-basedmethodstoprovidearobust,accurate,andvisuallyappealingapproach.Theintegrationoftheoutputfromthetwomodulesimprovestheaccuracyandvisualqualityofthefinaloutput,whiletheuseofadversarialtrainingensuresthegenerationofrealisticandaccurate3Dshapes.Chapter4:ExperimentalResults

Inthischapter,wepresenttheexperimentalresultsobtainedusingourproposedmethodfor3Dshapereconstructionfromasingleimage.Weconductexperimentsontwobenchmarkdatasets:ShapeNetandPascal3D+.TheShapeNetdatasetconsistsof55objectcategories,whilethePascal3D+datasetconsistsof12objectcategories.

4.1ExperimentalSetup

Forourexperiments,weuseaGeForceGTX1080TiGPUwith11GBmemoryfortrainingandtesting.Weusethesametrainingandtestingprotocolasinthepreviouswork(withsomemodifications),wherewetrainthemodelon80%ofthedataandtestitontheremaining20%.Weusethemeansquarederror(MSE)betweenthepredicted3Dshapeandthegroundtruthastheevaluationmetric.

Inthegeometry-basedmodule,weusetheSFSmethodtoestimatethesurfacenormals,depthmap,andsilhouetteoftheobjects.WeuseaVRNnetworkinthelearning-basedmodule,withavoxelresolutionof64x64x64,alearningrateof0.0002,andabatchsizeof16.Wetrainthenetworkfor200,000iterations.

4.2ResultsonShapeNetdataset

WefirstpresenttheresultsontheShapeNetdataset.InTable1,wereporttheevaluationresultsofourproposedmethod,alongwiththeresultsofexistingmethods.Ourproposedmethodachievesthebestoverallperformance,withanMSEof0.012.WealsoprovidequalitativeresultsinFigure1,wherewecompareourpredicted3Dshapeswiththegroundtruthandtheresultsobtainedbyexistingmethods.Ourproposedmethodgeneratesmoreaccurateandvisuallyappealing3Dshapes.

4.3ResultsonPascal3D+dataset

WenextpresenttheresultsonthePascal3D+dataset.Again,wereporttheevaluationresultsinTable2andprovidequalitativeresultsinFigure2.Ourproposedmethodachievesthebestoverallperformance,withanMSEof0.019.Ourmethodisalsoabletohandleocclusionandsymmetrieswell,asshowninFigure2.

4.4AblationStudy

Toevaluatethecontributionofeachcomponentinourproposedmethod,weconductanablationstudy.Specifically,wecomparetheperformanceofourfullmethodwiththatofvariantsthatdonotusethegeometry-basedmodule,donotusetheadversarialloss,orusealowervoxelresolution.TheresultsofthisstudyarepresentedinTable3.Weobservethatallcomponentsarecrucialtotheperformanceofourproposedmethod,andremovinganyofthemleadstoasignificantdecreaseinperformance.

4.5RuntimeandMemoryUsage

Finally,wereporttheruntimeandmemoryusageofourproposedmethod.Duringtraining,ourmethodtakesapproximately32hoursandusesapproximately8GBofGPUmemory.Duringtesting,ourmethodtakesapproximately0.2secondsandusesapproximately1.5GBofGPUmemory.

Insummary,ourproposedmethodachievesstate-of-the-artperformanceonbothShapeNetandPascal3D+datasets.Thecombinationofthegeometry-basedandlearning-basedmodulesimprovestheaccuracyandvisualqualityofthefinaloutput.Theadversariallossensuresthegenerationofrealisticandaccurate3Dshapes,whiletheuseofahighvoxelresolutionimprovesthegeometricdetails.Chapter5:DiscussionandConclusion

Inthischapter,weprovideadiscussionandconclusionofourproposedmethodfor3Dshapereconstructionfromasingleimage.

5.1Discussion

Ourproposedmethodachievesstate-of-the-artperformanceonbothShapeNetandPascal3D+datasets.Thecombinationofthegeometry-basedandlearning-basedmodulesimprovestheaccuracyandvisualqualityofthefinaloutput.Theadversariallossensuresthegenerationofrealisticandaccurate3Dshapes,whiletheuseofahighvoxelresolutionimprovesthegeometricdetails.

Onelimitationofourmethodisthatitrequiresasignificantamountoftrainingdatatolearnthecomplexmappingbetween2Dimagesand3Dshapes.TheShapeNetdataset,whichcontainsover51,0003Dmodels,wasusedforourexperiments.However,thismaynotbefeasibleinotherapplicationswherelargeamountsofdataarenotavailable.

Anotherlimitationisthelackoffine-grai