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基于DenseNet的单目图像深度估计.docx

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基于DenseNet的单目图像深度估计 Title:SingleImageDepthEstimationBasedonDenseNet Abstract: DepthestimationisacriticaltaskincomputervisionthataimstoestimatethedepthinformationofascenefromasingleRGBimage.Thispaperproposesanovelapproachforsingleimagedepthestimation,combiningthepowerofDenseNetarchitecturewithdeeplearningtechniques.TheproposedmethodleveragestherichfeatureextractioncapabilitiesofDenseNettolearndenseandcompactrepresentations,allowingforaccuratedepthestimation.Experimentalresultsdemonstratetheeffectivenessandsuperiorityoftheproposedapproachcomparedtoexistingmethods. 1.Introduction Depthperceptionisanimportantaspectofhumanvisualperceptionandiscrucialformanyapplicationsincomputervision,includingautonomousdriving,augmentedreality,androbotics.Traditionalapproachestodepthestimationrelyonstereoimagingorstructuredlight,whichrequireadditionalsensorsorcomplexsetup.Singleimagedepthestimation,ontheotherhand,aimstoestimatedepthinformationfromasingleRGBimage,eliminatingtheneedforadditionalsensorsandfacilitatingreal-worldapplications. 2.RelatedWork Severalapproacheshavebeenproposedforsingleimagedepthestimation,includingtraditionalhandcraftedfeature-basedmethodsandmorerecentdeeplearning-basedtechniques.Handcraftedfeature-basedmethodsoftenrelyonengineeringfeaturesandemploymachinelearningalgorithmstoestimatedepth.However,suchmethodsstruggletocapturecomplexrelationshipsandlackgeneralizability.Deeplearning-basedtechniques,ontheotherhand,haveshowngreatpromiseindepthestimation,leveragingthepowerofconvolutionalneuralnetworks(CNNs)tolearncomplexfeaturesfromrawimagedata. 3.DenseNetArchitecture DenseNetisastate-of-the-artCNNarchitecturethathasdemonstratedexcellentperformanceinvariouscomputervisiontasks,suchasimageclassificationandobjectdetection.DenseNetintroducestheconceptofdenseconnections,whereeachlayerisdirectlyconnectedtoeveryotherlayerinafeed-forwardmanner.Thisdenseconnectivityenablesfeaturereuseandpromotesgradientflowthroughoutthenetwork,leadingtoimprovedfeaturelearningandcompactnetworkr