public TriangulateRefineMetricLS(double convergenceTol, int maxIterations) { this.convergenceTol = convergenceTol; this.maxIterations = maxIterations; minimizer = FactoryOptimization.levenbergMarquardt(null,false); }
public TriangulateRefineEpipolarLS(double convergenceTol, int maxIterations) { this.maxIterations = maxIterations; this.convergenceTol = convergenceTol; minimizer = FactoryOptimization.levenbergMarquardt( null,false); }
public TriangulateRefineProjectiveLS(double convergenceTol, int maxIterations) { this.convergenceTol = convergenceTol; this.maxIterations = maxIterations; minimizer = FactoryOptimization.levenbergMarquardt(null,false); }
public LeastSquaresHomography(double convergenceTol, int maxIterations, ModelObservationResidualN residuals ) { this.maxIterations = maxIterations; this.convergenceTol = convergenceTol; this.func = new ResidualsEpipolarMatrixN(null,residuals); minimizer = FactoryOptimization.levenbergMarquardt(null,false); }
/** * Creates a trifocal tensor estimation algorithm. * * @param config configuration for the estimator * @return Trifocal tensor estimator */ public static Estimate1ofTrifocalTensor trifocal_1( @Nullable ConfigTrifocal config ) { if( config == null ) { config = new ConfigTrifocal(); } switch( config.which ) { case LINEAR_7: return new WrapTrifocalLinearPoint7(); case ALGEBRAIC_7: ConfigConverge cc = config.converge; UnconstrainedLeastSquares optimizer = FactoryOptimization.levenbergMarquardt(null, false); TrifocalAlgebraicPoint7 alg = new TrifocalAlgebraicPoint7(optimizer, cc.maxIterations,cc.ftol,cc.gtol); return new WrapTrifocalAlgebraicPoint7(alg); } throw new IllegalArgumentException("Unknown type "+config.which); }
public LeastSquaresFundamental(ModelCodec<DMatrixRMaj> paramModel, double convergenceTol, int maxIterations, boolean useSampson) { this.paramModel = paramModel; this.maxIterations = maxIterations; this.convergenceTol = convergenceTol; param = new double[paramModel.getParamLength()]; ModelObservationResidual<DMatrixRMaj,AssociatedPair> residual; if( useSampson ) residual = new FundamentalResidualSampson(); else residual = new FundamentalResidualSimple(); func = new ResidualsEpipolarMatrix(paramModel,residual); minimizer = FactoryOptimization.levenbergMarquardt(null,false); }
public PnPRefineRodrigues(double convergenceTol, int maxIterations ) { this.maxIterations = maxIterations; this.convergenceTol = convergenceTol; this.minimizer = FactoryOptimization.levenbergMarquardt(null,false); func = new ResidualsCodecToMatrix<>(paramModel, new PnPResidualReprojection(), new Se3_F64()); param = new double[paramModel.getParamLength()]; }
UnconstrainedLeastSquares<DMatrixRMaj> optimizer = FactoryOptimization.levenbergMarquardt(null, true);
ConfigLevenbergMarquardt config = new ConfigLevenbergMarquardt(); config.mixture = 0; optimizer = FactoryOptimization.levenbergMarquardt(config,true);