【《光场成像及广场深度估计研究的国内外文献综述》3400字】

[48]提出了FocalStackNet，学习深度的语义特征和中心视角图像的局部结构信息，进而预测光场深度。上述方法表明，焦点线索有助于提高深度估计的精确度。但是这些方法忽略了焦点切片之间的空间相关性，无法充分学习与深度相关的多聚焦特征，预测的深度图中包含大量噪声。虽然利用卷积神经网络提取多聚焦特征的方法已经显示出光场在深度估计问题中的有效性。但是受限于光场数据资源较少问题，算法在如何有效学习多聚焦特征方面仍然具有提升的空间。当面临复杂场景，例如场景中出现遮挡或噪声严重区域时，重聚焦图像中感兴趣的物体会叠加遮挡物虚化伪影，严重影响深度估计的质量。此外如何筛选焦点堆栈中重要的信息，学习焦点切片之间的空间相关性，从而关联焦点与深度，对减少光场数据信息冗余，提高预测深度图的精度尤为重要。参考文献SongS,XiaoJ.TrackingRevisitedUsingRGBDCamera:UnifiedBenchmarkandBaselines[C].Internationalconferenceoncomputervision,2013:233-240.SilbermanN,HoiemDW,KohliP,etal.IndoorsegmentationandsupportinferencefromRGBDimages[C].Europeanconferenceoncomputervision,2012:746-760.SilbermanN,HoiemDW,KohliP,etal.IndoorsegmentationandsupportinferencefromRGBDimages[C].Europeanconferenceoncomputervision,2012:746-760.CriminisiA,ShottonJ.EfficientHumanPoseEstimationfromSingleDepthImages[J].SpringerLondon,2013,10.1007/978-1-4471-4929-3(Chapter13):175-192.SharpT.TheVitruvianmanifold:Inferringdensecorrespondencesforone-shothumanposeestimation[C].IEEEConferenceonComputerVisionandPatternRecognition(CVPR).IEEE,2012.Mur-ArtalR,TardósJD.Orb-slam2:Anopen-sourceslamsystemformonocular,stereo,andrgb-dcameras[J].IEEETransactionsonRobotics,2017,33(5):1255-1262.曾仕峰,吴锦均,叶智文,等.基于ROS的无人驾驶智能车[J].物联网技术,2020,10(06):62-63+66.ChenCF,BolasM,RosenbergES.Rapidcreationofphotorealisticvirtualrealitycontentwithconsumerdepthcameras[C].VirtualReality.IEEE,2017.SrinivasanPP,GargR,WadhwaN,etal.Aperturesupervisionformonoculardepthestimation[C].ProceedingsoftheIEEEConferenceonComputerVisionandPatternRecognition.2018:6393-6401.MaF,KaramanS.Sparse-to-dense:Depthpredictionfromsparsedepthsamplesandasingleimage[C].InternationalConferenceonRoboticsandAutomation(ICRA).IEEE,2018:4796-4803.RobertsR,SinhaSN,SzeliskiR,etal.Structurefrommotionforsceneswithlargeduplicatestructures[C].IEEEConferenceonComputerVisionandPatternRecognition(CVPR).IEEE,2011.KendallA,MartirosyanH,DasguptaS,etal.End-to-EndLearningofGeometryandContextforDeepStereoRegression[J].IEEE,2017.PangJ,SunW,RenJS,etal.CascadeResidualLearning:ATwo-stageConvolutionalNeuralNetworkforStereoMatching[J].IEEEComputerSociety,2017.HeberS,PockT.ConvolutionalNetworksforShapefromLightField[C].IEEEConferenceonComputerVisionandPatternRecognition(CVPR).IEEE,2016.HeberS,WeiY,PockT.NeuralEPI-VolumeNetworksforShapefromLightField[C].IEEEInternationalConferenceonComputerVision(ICCV).IEEE,2017.Jeon.DepthfromaLightFieldImagewithLearning-BasedMatchingCosts[J].IEEETransactionsonPatternAnalysisandMachineIntelligence,2019,41(2):297-310.JohannsenO,SulcA,GoldlueckeB.WhatSparseLightFieldCodingRevealsaboutSceneStructure[C].IEEEConferenceonComputerVisionandPatternRecognition(CVPR).IEEE,2016.SabaterN,SeifiM,DrazicV,etal.AccurateDisparityEstimationforPlenopticImages[C].LightFieldsforComputerVisioninConjunctionwithEccv.SpringerInternationalPublishing,2014.BishopTE,FavaroP.Full-resolutiondepthmapestimationfromanaliasedplenopticlightfield[C].AsianConferenceonComputerVision.Springer,Berlin,Heidelberg,2010:186-200.KimC,ZimmerH,PritchY,etal.Scenereconstructionfromhighspatio-angularresolutionlightfields[J].ACMTransactionsonGraphics,2013,32(4):1-12.ZhangYB,LvHJ,LiuYB,