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基于联合SIFT和SURF特征的三维表面重建.docx

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基于联合SIFT和SURF特征的三维表面重建 Title:3DSurfaceReconstructionbasedonJointSIFTandSURFFeatures Abstract: Three-dimensional(3D)surfacereconstructionplaysacrucialroleincomputervisionandroboticsapplications.Inthispaper,weproposeanovelapproachfor3DsurfacereconstructionbyleveragingthejointfeaturesofScale-InvariantFeatureTransform(SIFT)andSpeededUpRobustFeatures(SURF).SIFTisrobusttoimagetransformations,whileSURFisknownforitsefficiencyandscaleinvariance.Bycombiningthestrengthsofbothfeatures,ourproposedmethodaimstoachieveenhancedaccuracyandefficiencyin3Dsurfacereconstruction. 1.Introduction Three-dimensionalsurfacereconstructionisafundamentalproblemincomputervisionandhaswide-rangingapplicationsinfieldssuchasrobotics,virtualreality,andautonomousnavigation.Thegoalistogenerateadetailed3Drepresentationofanobjectorenvironmentfromacollectionof2Dimagesorpointclouddata.Traditionalmethodsoftensufferfromdifficultiesindealingwithocclusions,lightingvariations,andvaryingscales.Inthispaper,wepresentanovelapproachthatcombinestheSIFTandSURFfeaturestoovercomethesechallengesandachieveimproved3Dsurfacereconstruction. 2.RelatedWork Previousresearchon3Dsurfacereconstructionhasfocusedonusingvariousfeatureextractionalgorithms,suchasSIFT,SURF,andtheirvariants.SIFTfeaturesarerobusttochangesinscaling,rotation,andaffinetransformations,makingthemsuitablefor3Dreconstruction.SURFfeatures,ontheotherhand,offerfastfeaturedetectionandextraction,alongwithscaleinvariance.SomemethodshaveusedeitherSIFTorSURFindependently,butthereisalackofresearchontheirjointutilizationfor3Dsurfacereconstruction. 3.Methodology Ourproposedmethodologyisdividedintotwomainstages:featureextractionand3Dsurfacereconstruction. 3.1FeatureExtraction Inthisstage,SIFTandSURFfeaturesareextractedfromtheinput2Dimages.SIFTdetectsanddescribeskeypointsthatareinvarianttoscaling,rotation,andaffinetransformations.SURF,ontheotherhand,detectsinterestpointsbasedontheHessianmatrixandcomputestheSURFdescriptor.BycombiningSIFTandSURFfeatures,weleveragethecomplementar