Model-based recognition and motion tracking depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. This paper extends current methods of parameter solving to handle objects with

FittingParameterizedThree-DimensionalModelstoImages

DavidG.Lowe

ComputerScienceDepartment

UniversityofBritishColumbia

Vancouver,B.C.,CanadaV6T1Z4

Email:lowe@cs.ubc.ca

Abstract

Model-basedrecognitionandmotiontrackingdependsupontheabilitytosolveforprojectionandmodelparametersthatwillbest ta3-Dmodeltomatching2-Dimagefeatures.Thispaperextendscurrentmethodsofparametersolvingtohandleobjectswitharbitrarycurvedsurfacesandwithanynumberofinternalpa-rametersrepresentingarticulations,variabledimensions,orsurfacedeformations.Numericalstabilizationmethodsaredevelopedthattakeaccountofinherentinac-curaciesintheimagemeasurementsandallowusefulsolutionstobedeterminedevenwhentherearefewermatchesthanunknownparameters.TheLevenberg-Marquardtmethodisusedtoalwaysensureconvergenceofthesolution.Thesetechniquesallowmodel-basedvisiontobeusedforamuchwiderclassofprob-lemsthanwaspossiblewithpreviousmethods.Theirapplicationisdemonstratedfortrackingthemotionofcurved,parameterizedobjects.

ThispaperhasbeenpublishedinIEEETransactionsonPatternAnalysisandMachineIntelligence,13,5(May1991),pp.441–450.

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