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1、StereoMatchingbyTrainingaConvolutionalNeuralNetworktoCompareImagePatchesJureZbontarˇZBONTAR@CS.NYU.EDUYannLeCunyYANN@CS.NYU.EDUCourantInstituteofMathematicalSciencesNewYorkUniversity715Broadway,NewYork,NY10003,USAAbstractWepresentamethodforextractingdepthinformationf
2、romarectifiedimagepair.Ourapproachfocusesonthefirststageofmanystereoalgorithms:thematchingcostcomputation.Weapproachtheproblembylearningasimilaritymeasureonsmallimagepatchesusingaconvolutionalneuralnetwork.Trainingiscarriedoutinasupervisedmannerbyconstructingabinaryclas
3、sificationdatasetwithexamplesofsimilaranddissimilarpairsofpatches.Weexaminetwonetworkarchitec-turesforthistask:onetunedforspeed,theotherforaccuracy.Theoutputoftheconvolutionalneuralnetworkisusedtoinitializethestereomatchingcost.Aseriesofpost-processingstepsfol-low:cros
4、s-basedcostaggregation,semiglobalmatching,aleft-rightconsistencycheck,subpixelenhancement,amedianfilter,andabilateralfilter.WeevaluateourmethodontheKITTI2012,KITTI2015,andMiddleburystereodatasetsandshowthatitoutperformsotherapproachesonallthreedatasets.Keywords:stereo,m
5、atchingcost,similaritylearning,supervisedlearning,convolutionalneuralnetworks1.IntroductionConsiderthefollowingproblem:giventwoimagestakenfromcamerasatdifferenthorizontalpo-sitions,wewishtocomputethedisparitydforeachpixelintheleftimage.Disparityreferstothedifferencein
6、horizontallocationofanobjectintheleftandrightimage—anobjectatposition(x;y)intheleftimageappearsatposition(x d;y)intherightimage.Ifweknowthedisparityofanobjectwecancomputeitsdepthzusingthefollowingrelation:arXiv:1510.05970v1[cs.CV]20Oct2015fBz=;dwherefisthefocallengtho
7、fthecameraandBisthedistancebetweenthecameracenters.Figure1depictstheinputtoandtheoutputfromourmethod.Thedescribedproblemofstereomatchingisimportantinmanyfieldssuchasautonomousdriving,robotics,intermediateviewgeneration,and3Dscenereconstruction.Accordingtothetax-onomyof
8、ScharsteinandSzeliski(2002),atypicalstereoalgorithmconsistsoffoursteps:matching.JureZbontarisalsowiththeˇFacultyofComputera