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基于自适应切片的点云压缩算法.docx

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基于自适应切片的点云压缩算法 1.Introduction Withtheincreasinguseof3Dpointcloudsinvariousapplicationssuchasremotesensing,robotics,andautonomousdriving,thereisaneedforefficientcompressionalgorithmstocompressthepointclouddata.Manycompressionalgorithmshavebeenproposedintheliterature,butmostofthemarenotefficientenoughtohandlelarge-scalepointclouddata.Inrecentyears,severaladaptiveslicing-basedpointcloudcompressionalgorithmshavebeenproposedthatcanhandlelarge-scale3Dpointclouddatawithhighercompressionratios,makingthemmoresuitableforpracticalapplications.Inthispaper,wepresentanoverviewofadaptiveslicing-basedpointcloudcompressionalgorithms,focusingontheiradvantagesandlimitations. 2.AdaptiveSlicing-basedPointCloudCompression Thebasicideabehindadaptiveslicing-basedpointcloudcompressionistodividethepointcloudintosmallrectangularsubsetsknownasslicesandcompresseachsliceseparately.Thisapproachhastheadvantageofallowingthecompressionalgorithmtoadapttodifferentpointdensitiesandlocalgeometriccharacteristicsofthepointcloud.Theadaptiveslicing-basedpointcloudcompressionalgorithmscanbeclassifiedintotwocategories:staticanddynamicslicingalgorithms. 2.1StaticSlicingAlgorithms Thestaticslicingalgorithmsdividethepointcloudintoequalsizedslices,irrespectiveofthepointdensity.Thesizeofeachsliceisdeterminedbasedonthenumberofpointsinthepointcloud.Forexample,ifthetotalnumberofpointsinthepointcloudisNandthedesiredslicesizeisK,thenthepointcloudisdividedintoN/Kslices.Thestaticslicingalgorithmsaresimpletoimplementandcanachievehighcompressionratiosforpointcloudswithhomogenouspointdensities.However,theycanbelessefficientforpointcloudswithvaryingdensitiesastheymayproducesliceswithsignificantvariationsinthenumberofpoints. 2.2DynamicSlicingAlgorithms Thedynamicslicingalgorithmsdividethepointcloudintovariablesizedslicesbasedonthelocalpointdensity.Thesizeofeachsliceisdeterminedbasedonanadaptivethresholdthatdependsonthepointdensity.Forexample,aslicewithahighpointdensitywillhaveasmallersizecomparedtoaslicewithalowpointdensity.T