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基于Kohonen网络的点群综合研究(英文).docx

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基于Kohonen网络的点群综合研究(英文) ResearchonPointCloudSynthesisBasedonKohonenNetwork Abstract: Pointcloudsynthesisisanessentialresearchtopicincomputerscienceandengineering.Kohonennetworkisapopularmethodforpointcloudsynthesis.Inthisresearch,weinvestigatetheapplicationofKohonennetworkinpointcloudsynthesis. WedesignaframeworkforpointcloudsynthesisbasedonKohonennetworkandevaluateitsperformanceusingvariousmetrics.OurresultsindicatethattheKohonennetworkmethodishighlyeffectiveingeneratinghigh-qualitypointclouds. Introduction: Pointcloudisanessentialdataformatfor3Dmodelingandcomputervisiontasks.Pointcloudsynthesisreferstothegenerationofnewpointcloudsbasedonexistingones.Thistechniqueisincreasinglypopularforgeneratingrealistic3Dmodelsofobjects,buildings,andlandscapes.TheKohonennetwork,alsoknownastheself-organizingmap(SOM),isamachinelearningalgorithmthatmimicsthestructuralorganizationofthehumanbrain.Thisneuralnetworkhasbeenwidelyappliedinimageprocessing,patternrecognition,anddataanalysis. Methods: ToapplytheKohonennetworktopointcloudsynthesis,wedesignaframeworkconsistingoffourmainstages:datapreprocessing,networkconfiguration,training,andpointcloudgeneration.Inthedatapreprocessingstage,wereadandpreprocesstheinputpointcloudsbyremovingoutliers,normalizingthepointcoordinates,andconvertingthemtoasuitableformat.Inthenetworkconfigurationstage,wedesignatopologyoftheKohonennetworkandspecifyitshyperparameters,suchasthelearningrate,neighborhoodfunction,andnumberofoutputnodes.Inthetrainingstage,weusetheinputpointcloudstotraintheKohonennetworkbyadjustingthesynapticweights.Finally,inthepointcloudgenerationstage,wegeneratenewpointcloudsbysamplingtheoutputnodesoftheKohonennetworkandapplyingadecodingfunction. Results: WeevaluatetheperformanceoftheKohonennetworkmethodforpointcloudsynthesisusingvariousmetrics,suchasroot-mean-squareerror(RMSE),Chamferdistance,andvisualinspection.OurresultsindicatethattheKohonennetworkmethodishighlyeffectiveingeneratinghigh-qualitypointcloudsthatarevisuallyandstructurallysim