efficientransac_Efficient RANSAC for Point-Cloud Shape Detection

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本文介绍了一种自动高效的算法，用于检测无组织点云中的基本形状。该算法将点云分解为一组固有形状及剩余点的混合结构。通过随机采样，能够快速检测平面、球体、圆柱体、圆锥体和环面等形状。即使在存在大量异常值和高噪声的情况下，算法也表现出良好的鲁棒性。

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Volume0(1981),Number0pp.1–12

Ef cientRANSACforPoint-CloudShapeDetection

RuwenSchnabel

RolandWahl

ReinhardKlein

UniversitätBonn,ComputerGraphicsGroup

Abstract

Inthisworkwepresentanautomaticalgorithmtodetectbasicshapesinunorganizedpointclouds.Thealgorithmdecomposesthepointcloudintoaconcise,hybridstructureofinherentshapesandasetofremainingpoints.Eachdetectedshapeservesasaproxyforasetofcorrespondingpoints.Ourmethodisbasedonrandomsamplinganddetectsplanes,spheres,cylinders,conesandtori.Formodelswithsurfacescomposedofthesebasicshapesonly,e.g.CADmodels,weautomaticallyobtainarepresentationsolelyconsistingofshapeproxies.Wedemonstratethatthealgorithmisrobusteveninthepresenceofmanyoutliersandahighdegreeofnoise.Theproposedmethodscaleswellwithrespecttothesizeoftheinputpointcloudandthenumberandsizeoftheshapeswithinthedata.Evenpointsetswithseveralmillionsofsamplesarerobustlydecomposedwithinlessthanaminute.Moreoverthealgorithmisconceptuallysimpleandeasytoimplement.Applicationareasincludemeasurementofphysicalparameters,scanregistration,surfacecompression,hybridrendering,shapeclassi cation,meshing,simpli cation,approximationandreverseengineering.

CategoriesandSubjectDescriptors(accordingtoACMCCS):I.4.8[ImageProcessingandComputerVision]:SceneAnalysisShape;SurfaceFitting;I.3.5[ComputerGraphics]:ComputationalGeometryandObjectModelingCurve,surface,solid,andobjectrepresentations

1.Introduction

Duetotheincreasingsizeandcomplexityofgeometricdatasetsthereisanever-growingdemandforconciseandmean-ingfulabstractionsofthisdata.Especiallywhendealingwithdigitizedgeometry,e.g.acquiredwithalaserscanner,nohandlesformodi cationofthedataareavailabletotheuserotherthanthedigitizedpointsthemselves.However,inor-dertobeabletomakeuseofthedataeffectively,therawdigitizeddatahastobeenrichedwithabstractionsandpos-siblysemanticinformation,providingtheuserwithhigher-levelinteractionpossibilities.Onlysuchhandlescanpro-videtheinteractionrequiredforinvolvededitingprocesses,suchasdeleting,movingorresizingcertainpartsandhencecanmakethedatamorereadilyusableformodelingpur-poses.Ofcourse,traditionalreverseengineeringapproachescanprovidesomeoftheabstractionsthatweseek,butusu-allyreverseengineeringfocuseson ndingareconstructionoftheunderlyinggeometryandtypicallyinvolvesquitete-dioususerinteraction.Thisisnotjusti edinasettingwhere

acompleteanddetailedreconstructionisnotrequiredatall,orshalltakeplaceonlyaftersomebasiceditingoperationshavebeenappliedtothedata.Ontheotherhand,detectinginstancesofasetofprimitivegeometricshapesinthepointsampleddataisameanstoquicklyderivehigherlevelsofab-straction.ForexampleinFig.1patchesofprimitiveshapesprovideacoarseapproximationofthegeometrythatcouldbeusedtocompressthepoint-cloudveryeffectively.Anotherproblemarisingwhendealingwithdigitizedgeom-etryistheoftenhugesizeofthedatasets.Thereforetheef ciencyofalgorithmsinferringabstractionsofthedataisofutmostimportance,especiallyininteractivesettings.Thus,inthispaperwefocusespeciallyon ndinganef -cientalgorithmforpoint-cloudshapedetection,inordertobeabletodealevenwithlargepoint-clouds.OurworkisahighperformanceRANSAC[FB81]algorithmthatiscapa-bletoextractavarietyofdifferenttypesofprimitiveshapes,whileretainingsuchfavorablepropertiesoftheRANSACparadigmasrobustness,generalityandsimplicity.Attheheartofouralgorithmareanovel,hierarchicallystructuredsamplingstrategyforcandidateshapegenerationaswellasanovel,lazycostfunctionevaluationscheme,whichsignif-

e-mail:{schnabel,wahl,rk}@cs.uni-bonn.de

cTheEurographicsAssociationandBlackwellPublishing2007.PublishedbyBlackwell

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