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1、DroughtStressClassificationusing3DPlantModelsSiddharthSrivastava∗†,SwatiBhugra∗†,BrejeshLall†,SantanuChaudhury†‡†DepartmentofElectricalEngineering,IndianInstituteofTechnology,Delhi,India‡CentralElectronicsEngineeringResearchInstitute,Pilani,India{eez127506
2、,eez138301,brejesh,santanuc}@ee.iitd.ac.inAbstractemployedvisualhullalgorithmfor3DreconstructionwithastaticplantcanopyandcamerarotatingatfixedheightQuantificationofphysiologicalchangesinplantscanarounditusingaturntablesetup.CaiandMiklavcic[6]capturedifferen
3、tdroughtmechanismsandassistinselec-utilized2Dskeletonstoovercomethedifficultiessuchastionoftolerantvarietiesinahighthroughputmanner.Inoverlappingplantpartsandbrokensegmentsforasmooththiscontext,anaccurate3Dmodelofplantcanopyprovides3Dreconstruction.Kumaret
4、al.[19]presentedamirrorareliablerepresentationfordroughtstresscharacteriza-basedsetupthatenabledthebackoftheplanttobecapturedtionincontrasttousing2Dimages.Inthispaper,wepro-inthefrontview,however,itresultedinlossofresolution.poseanovelend-to-endpipelinein
5、cluding3Dreconstruc-Visualhullmethodsforreconstructionofthinleafsurfacestion,segmentationandfeatureextraction,leveragingdeepwithdiscontinuitiesinplantcanopyoftenresultinoveresti-neuralnetworksatvariousstages,fordroughtstressstudy.matedmodels.Incontrasttot
6、hepreviouslymentionedap-Toovercomethehighdegreeofself-similaritiesandself-proaches,authorsin[37]employedmulti-viewstereoandocclusionsinplantcanopy,priorknowledgeofleafshapeStructurefromMotion(SfM)toobtaininitialsparsepointbasedonfeaturesfromdeepsiamesenet
7、workareusedtocloudandthenpatchbasedMVS(PMVS)wasusedtoob-constructanaccurate3Dmodelusingstructurefrommo-taindensepointcloudstorepresentbasil,tomatoplantsandtiononwheatplants.Thedroughtstressischaracterizedmintleaves.Louetal.[23]alsoutilizedSfMfollowedwitha
8、deepnetworkbasedfeatureaggregation.Wecom-bystereomatchinganddepth-mapmergingprocessfor3Dparetheproposedmethodologyonseveraldescriptors,andplantmodeling.Thesestudiesaresuitableforplantswithshowthatthenetworkoutperfor
