boofcv.factory.geo

Class FactoryMultiView

java.lang.Object

boofcv.factory.geo.FactoryMultiView

public class FactoryMultiView
extends java.lang.Object

Factory for creating abstracted algorithms related to multi-view geometry

Constructor Summary

Constructors

Constructor and Description
FactoryMultiView()

Method Summary

Methods

Modifier and Type	Method and Description
static BundleAdjustmentCalibrated	bundleCalibrated(double tol, int maxIterations) Creates bundle adjustment for a camera with a know and fixed intrinsic calibration
static Estimate1ofEpipolar	computeFundamental_1(EnumEpipolar which, int numRemoveAmbiguity) Similar to computeFundamental_N(boofcv.factory.geo.EnumEpipolar), but it returns only a single hypothesis.
static EstimateNofEpipolar	computeFundamental_N(EnumEpipolar which) Returns an algorithm for estimating a fundamental or essential matrix given a set of AssociatedPair in pixel coordinates.
static Estimate1ofEpipolar	computeHomography(boolean normalize) Returns an algorithm for estimating a homography matrix given a set of AssociatedPair.
static Estimate1ofPnP	computePnP_1(EnumPNP which, int numIterations, int numTest) Created an estimator for the P3P problem that selects a single solution by considering additional observations.
static EstimateNofPnP	computePnP_N(EnumPNP which, int numIterations) Creates an estimator for the PnP problem that uses only three observations, which is the minimal case and known as P3P.
static Estimate1ofPnP	computePnPwithEPnP(int numIterations, double magicNumber) Returns a solution to the PnP problem for 4 or more points using EPnP.
static PoseFromPairLinear6	computePoseFromPair() Estimate the camera motion give two observations and the 3D world coordinate of each points.
static Estimate1ofTrifocalTensor	estimateTrifocal_1(EnumTrifocal type, int iterations) Creates a trifocal tensor estimation algorithm.
static RefineEpipolar	refineFundamental(double tol, int maxIterations, EpipolarError type) Creates a non-linear optimizer for refining estimates of fundamental or essential matrices.
static RefineEpipolar	refineHomography(double tol, int maxIterations, EpipolarError type) Creates a non-linear optimizer for refining estimates of homography matrices.
static RefinePnP	refinePnP(double tol, int maxIterations) Refines a pose solution to the PnP problem using non-linear least squares..

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FactoryMultiView

public FactoryMultiView()

Method Detail

bundleCalibrated

public static BundleAdjustmentCalibrated bundleCalibrated(double tol,
                                          int maxIterations)

Creates bundle adjustment for a camera with a know and fixed intrinsic calibration

Parameters:

tol - Convergence tolerance. Try 1e-8

maxIterations - Maximum number of iterations. Try 200 or more

Returns:

Bundle Adjustment

computeHomography

public static Estimate1ofEpipolar computeHomography(boolean normalize)

Returns an algorithm for estimating a homography matrix given a set of AssociatedPair.

Parameters:

normalize - If input is in pixel coordinates set to true. False if in normalized image coordinates.

Returns:

Homography estimator.

See Also:

HomographyLinear4

refineHomography

public static RefineEpipolar refineHomography(double tol,
                              int maxIterations,
                              EpipolarError type)

Creates a non-linear optimizer for refining estimates of homography matrices.

Parameters:

tol - Tolerance for convergence. Try 1e-8

maxIterations - Maximum number of iterations it will perform. Try 100 or more.

Returns:

Homography refinement

See Also:

HomographyResidualSampson, HomographyResidualTransfer

computeFundamental_N

public static EstimateNofEpipolar computeFundamental_N(EnumEpipolar which)

Returns an algorithm for estimating a fundamental or essential matrix given a set of AssociatedPair in pixel coordinates. The number of hypotheses returned and minimum number of samples is dependent on the implementation. The ambiguity from multiple hypotheses can be resolved using other sample points and testing additional constraints.

All estimated epipolar matrices will have the following constraint:
x'*F*x = 0, where F is the epipolar matrix, x' = currLoc, and x = keyLoc.

There are more differences between these algorithms than the minimum number of sample points. Consult the literature for information on critical surfaces which will work or not work with each algorithm. In general, algorithm which require fewer samples have less issues with critical surfaces than the 8-point algorithm.

IMPORTANT: When estimating a fundamental matrix use pixel coordinates. When estimating an essential matrix use normalized image coordinates from a calibrated camera.

IMPORTANT. The number of allowed sample points varies depending on the algorithm. The 8 point algorithm can process 8 or more points. Both the 5 an 7 point algorithms require exactly 5 and 7 points exactly. In addition the 5-point algorithm is only for the calibrated (essential) case.

Parameters:

which - Specifies which algorithm is to be created

Returns:

Fundamental or essential estimation algorithm that returns multiple hypotheses.

See Also:

EssentialNister5, FundamentalLinear7, FundamentalLinear8

computeFundamental_1

public static Estimate1ofEpipolar computeFundamental_1(EnumEpipolar which,
                                       int numRemoveAmbiguity)

Similar to computeFundamental_N(boofcv.factory.geo.EnumEpipolar), but it returns only a single hypothesis. If the underlying algorithm generates multiple hypotheses they are resolved by considering additional sample points. For example, if you are using the 7 point algorithm at least one additional sample point is required to resolve that ambiguity. So 8 or more sample points are now required.

All estimated epipolar matrices will have the following constraint:
x'*F*x = 0, where F is the epipolar matrix, x' = currLoc, and x = keyLoc.

See computeFundamental_N(boofcv.factory.geo.EnumEpipolar) for a description of the algorithms and what 'minimumSamples' and 'isFundamental' do.

The 8-point algorithm already returns a single hypothesis and ignores the 'numRemoveAmbiguity' parameter. All other algorithms require one or more points to remove ambiguity. Understanding a bit of theory is required to understand what a good number of points is. If a single point is used then to select the correct answer that point must be in the inlier set. If more than one point, say 10, then not all of those points must be in the inlier set,

Parameters:

which - Specifies which algorithm is to be created

numRemoveAmbiguity - Number of sample points used to prune hypotheses. Ignored if only a single solution.

Returns:

Fundamental or essential estimation algorithm that returns a single hypothesis.

See Also:

GeoModelEstimatorNto1

refineFundamental

public static RefineEpipolar refineFundamental(double tol,
                               int maxIterations,
                               EpipolarError type)

Creates a non-linear optimizer for refining estimates of fundamental or essential matrices.

Parameters:

tol - Tolerance for convergence. Try 1e-8

maxIterations - Maximum number of iterations it will perform. Try 100 or more.

Returns:

RefineEpipolar

See Also:

FundamentalResidualSampson, FundamentalResidualSimple

estimateTrifocal_1

public static Estimate1ofTrifocalTensor estimateTrifocal_1(EnumTrifocal type,
                                           int iterations)

Creates a trifocal tensor estimation algorithm.

Parameters:

type - Which algorithm.

iterations - If the algorithm is iterative, then this is the number of iterations. Try 200

Returns:

Trifocal tensor estimator

computePnP_N

public static EstimateNofPnP computePnP_N(EnumPNP which,
                          int numIterations)

Creates an estimator for the PnP problem that uses only three observations, which is the minimal case and known as P3P.

Parameters:

which - The algorithm which is to be returned.

numIterations - Number of iterations. Only used by some algorithms and recommended number varies significantly by algorithm.

Returns:

An estimator which can return multiple estimates.

computePnP_1

public static Estimate1ofPnP computePnP_1(EnumPNP which,
                          int numIterations,
                          int numTest)

Created an estimator for the P3P problem that selects a single solution by considering additional observations.

NOTE: EPnP has several tuning parameters and the defaults here might not be the best for your situation.

Parameters:

which - The algorithm which is to be returned.

numIterations - Number of iterations. Only used by some algorithms and recommended number varies significantly by algorithm.

numTest - How many additional sample points are used to remove ambiguity in the solutions. Not used if only a single solution is found.

Returns:

An estimator which returns a single estimate.

computePnPwithEPnP

public static Estimate1ofPnP computePnPwithEPnP(int numIterations,
                                double magicNumber)

Returns a solution to the PnP problem for 4 or more points using EPnP. Fast and fairly accurate algorithm. Can handle general and planar scenario automatically.

Parameters:

numIterations - If more then zero then non-linear optimization is done. More is not always better. Try 10

magicNumber - Affects how the problem is linearized. See comments in PnPLepetitEPnP. Try 0.1

Returns:

Estimate1ofPnP

See Also:

PnPLepetitEPnP

refinePnP

public static RefinePnP refinePnP(double tol,
                  int maxIterations)

Refines a pose solution to the PnP problem using non-linear least squares..

Parameters:

tol - Convergence tolerance. Try 1e-8

maxIterations - Maximum number of iterations. Try 200

computePoseFromPair

public static PoseFromPairLinear6 computePoseFromPair()

Estimate the camera motion give two observations and the 3D world coordinate of each points.

Returns:

PoseFromPairLinear6