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Augmented Reality and 3D reconstruction library
/*
* Copyright (C) 2015 Alberto Irurueta Carro ([email protected])
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.irurueta.ar.epipolar.estimators;
import com.irurueta.algebra.Matrix;
import com.irurueta.ar.epipolar.FundamentalMatrix;
import com.irurueta.ar.epipolar.refiners.FundamentalMatrixRefiner;
import com.irurueta.geometry.GeometryException;
import com.irurueta.geometry.Line2D;
import com.irurueta.geometry.Point2D;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
import com.irurueta.numerical.robust.InliersData;
import com.irurueta.numerical.robust.RobustEstimatorException;
import com.irurueta.numerical.robust.RobustEstimatorMethod;
import java.util.List;
/**
* This is an abstract class for algorithms to robustly find the best
* Fundamental matrix for provided collections of matched 2D points.
* Implementations of this class should be able to detect and discard outliers
* in order to find the best solution.
*/
public abstract class FundamentalMatrixRobustEstimator {
/**
* Default robust estimator method when none is provided.
*/
public static final RobustEstimatorMethod DEFAULT_ROBUST_METHOD =
RobustEstimatorMethod.PROSAC;
/**
* Default non-robust method to estimate a fundamental matrix.
*/
public static final FundamentalMatrixEstimatorMethod
DEFAULT_FUNDAMENTAL_MATRIX_ESTIMATOR_METHOD =
FundamentalMatrixEstimatorMethod.SEVEN_POINTS_ALGORITHM;
/**
* Indicates that result is refined by default using Levenberg-Marquardt
* fitting algorithm over found inliers.
*/
public static final boolean DEFAULT_REFINE_RESULT = true;
/**
* Indicates that covariance is not kept by default after refining result.
*/
public static final boolean DEFAULT_KEEP_COVARIANCE = false;
/**
* Default amount of progress variation before notifying a change in
* estimation progress. By default this is set to 5%.
*/
public static final float DEFAULT_PROGRESS_DELTA = 0.05f;
/**
* Minimum allowed value for progress delta.
*/
public static final float MIN_PROGRESS_DELTA = 0.0f;
/**
* Maximum allowed value for progress delta.
*/
public static final float MAX_PROGRESS_DELTA = 1.0f;
/**
* Constant defining default confidence of the estimated result, which is
* 99%. This means that with a probability of 99% estimation will be
* accurate because chosen sub-samples will be inliers.
*/
public static final double DEFAULT_CONFIDENCE = 0.99;
/**
* Default maximum allowed number of iterations.
*/
public static final int DEFAULT_MAX_ITERATIONS = 5000;
/**
* Minimum allowed confidence value.
*/
public static final double MIN_CONFIDENCE = 0.0;
/**
* Maximum allowed confidence value.
*/
public static final double MAX_CONFIDENCE = 1.0;
/**
* Minimum allowed number of iterations.
*/
public static final int MIN_ITERATIONS = 1;
/**
* List of 2D points corresponding to left view.
*/
protected List mLeftPoints;
/**
* List of 2D points corresponding to right view.
*/
protected List mRightPoints;
/**
* Listener to be notified of events such as when estimation starts, ends or
* its progress significantly changes.
*/
protected FundamentalMatrixRobustEstimatorListener mListener;
/**
* Indicates if this estimator is locked because an estimation is being
* computed.
*/
protected boolean mLocked;
/**
* Amount of progress variation before notifying a progress change during
* estimation.
*/
protected float mProgressDelta;
/**
* Amount of confidence expressed as a value between 0.0 and 1.0 (which is
* equivalent to 100%). The amount of confidence indicates the probability
* that the estimated result is correct. Usually this value will be close
* to 1.0, but not exactly 1.0.
*/
protected double mConfidence;
/**
* Maximum allowed number of iterations. When the maximum number of
* iterations is exceeded, result will not be available, however an
* approximate result will be available for retrieval.
*/
protected int mMaxIterations;
/**
* Data related to inliers found after estimation.
*/
protected InliersData mInliersData;
/**
* Indicates whether result must be refined using Levenberg-Marquardt
* fitting algorithm over found inliers.
* If true, inliers will be computed and kept in any implementation
* regardless of the settings.
*/
protected boolean mRefineResult;
/**
* Indicates whether covariance must be kept after refining result.
* This setting is only taken into account if result is refined.
*/
private boolean mKeepCovariance;
/**
* Estimated covariance of estimated fundamental matrix.
* This is only available when result has been refined and covariance is
* kept.
*/
private Matrix mCovariance;
/**
* Test line to compute epipolar residuals.
*/
private final Line2D mTestLine = new Line2D();
/**
* Internal non robust estimator of fundamental matrix.
*/
private FundamentalMatrixEstimator mFundMatrixEstimator;
/**
* Constructor.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
*/
protected FundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod) {
mProgressDelta = DEFAULT_PROGRESS_DELTA;
mConfidence = DEFAULT_CONFIDENCE;
mMaxIterations = DEFAULT_MAX_ITERATIONS;
mFundMatrixEstimator = FundamentalMatrixEstimator.create(
fundMatrixEstimatorMethod);
mRefineResult = DEFAULT_REFINE_RESULT;
mKeepCovariance = DEFAULT_KEEP_COVARIANCE;
}
/**
* Constructor.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
protected FundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod,
final FundamentalMatrixRobustEstimatorListener listener) {
this(fundMatrixEstimatorMethod);
mListener = listener;
}
/**
* Constructor with matched 2D points.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on right view.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
protected FundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod,
final List leftPoints, final List rightPoints) {
this(fundMatrixEstimatorMethod);
internalSetPoints(leftPoints, rightPoints);
}
/**
* Constructor with matched 2D points.
*
* @param fundMatrixEstimatorMethod method for non-robust fundamental matrix
* estimator.
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on right view.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
protected FundamentalMatrixRobustEstimator(
final FundamentalMatrixEstimatorMethod fundMatrixEstimatorMethod,
final List leftPoints, final List rightPoints,
final FundamentalMatrixRobustEstimatorListener listener) {
this(fundMatrixEstimatorMethod, listener);
internalSetPoints(leftPoints, rightPoints);
}
/**
* Constructor.
*/
protected FundamentalMatrixRobustEstimator() {
this(DEFAULT_FUNDAMENTAL_MATRIX_ESTIMATOR_METHOD);
}
/**
* Returns non-robust method to estimate a fundamental matrix.
*
* @return non-robust method to estimate a fundamental matrix.
*/
public FundamentalMatrixEstimatorMethod getNonRobustFundamentalMatrixEstimatorMethod() {
return mFundMatrixEstimator.getMethod();
}
/**
* Sets non-robust method to estimate a fundamental matrix.
*
* @param method non-robust method to estimate a fundamental matrix.
* @throws LockedException if this fundamental matrix estimator is locked.
*/
public void setNonRobustFundamentalMatrixEstimatorMethod(
final FundamentalMatrixEstimatorMethod method) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (method != getNonRobustFundamentalMatrixEstimatorMethod()) {
// if method changes, recreate internal non-robust fundamental matrix
// estimator
mFundMatrixEstimator = FundamentalMatrixEstimator.create(method);
}
}
/**
* Returns matched 2D points on left view.
*
* @return 2D points on left view.
*/
public List getLeftPoints() {
return mLeftPoints;
}
/**
* Returns matched 2D points on right view.
*
* @return 2D points on right view.
*/
public List getRightPoints() {
return mRightPoints;
}
/**
* Sets matched 2D points on both left and right views.
*
* @param leftPoints matched 2D points on left view.
* @param rightPoints matched 2D points on right view.
* @throws LockedException if this fundamental matrix estimator is locked.
* @throws IllegalArgumentException if provided matched points on left and
* right views do not have the same length or if their length is less than 7
* points.
*/
public void setPoints(final List leftPoints, final List rightPoints)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
internalSetPoints(leftPoints, rightPoints);
}
/**
* Returns reference to listener to be notified of events such as when
* estimation starts, ends or its progress significantly changes.
*
* @return listener to be notified of events.
*/
public FundamentalMatrixRobustEstimatorListener getListener() {
return mListener;
}
/**
* Sets listener to be notified of events such as when estimation starts,
* ends or its progress significantly changes.
*
* @param listener listener to be notified of events.
* @throws LockedException if robust estimator is locked.
*/
public void setListener(final FundamentalMatrixRobustEstimatorListener listener)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mListener = listener;
}
/**
* Indicates whether listener has been provided and is available for
* retrieval.
*
* @return true if available, false otherwise.
*/
public boolean isListenerAvailable() {
return mListener != null;
}
/**
* Returns boolean indicating if estimator is locked because estimation is
* under progress.
*
* @return true if estimator is locked, false otherwise.
*/
public boolean isLocked() {
return mLocked;
}
/**
* Returns amount of progress variation before notifying a progress change
* during estimation.
*
* @return amount of progress variation before notifying a progress change
* during estimation.
*/
public float getProgressDelta() {
return mProgressDelta;
}
/**
* Sets amount of progress variation before notifying a progress change
* during estimation.
*
* @param progressDelta amount of progress variation before notifying a
* progress change during estimation.
* @throws IllegalArgumentException if progress delta is less than zero or
* greater than 1.
* @throws LockedException if this estimator is locked because an estimation
* is being computed.
*/
public void setProgressDelta(final float progressDelta)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (progressDelta < MIN_PROGRESS_DELTA ||
progressDelta > MAX_PROGRESS_DELTA) {
throw new IllegalArgumentException();
}
mProgressDelta = progressDelta;
}
/**
* Returns amount of confidence expressed as a value between 0.0 and 1.0
* (which is equivalent to 100%). The amount of confidence indicates the
* probability that the estimated result is correct. Usually this value will
* be close to 1.0, but not exactly 1.0.
*
* @return amount of confidence as a value between 0.0 and 1.0.
*/
public double getConfidence() {
return mConfidence;
}
/**
* Sets amount of confidence expressed as a value between 0.0 and 1.0 (which
* is equivalent to 100%). The amount of confidence indicates the
* probability that the estimated result is correct. Usually this value will
* be close to 1.0, but not exactly 1.0.
*
* @param confidence confidence to be set as a value between 0.0 and 1.0.
* @throws IllegalArgumentException if provided value is not between 0.0 and
* 1.0.
* @throws LockedException if this estimator is locked because an estimator
* is being computed.
*/
public void setConfidence(final double confidence)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (confidence < MIN_CONFIDENCE || confidence > MAX_CONFIDENCE) {
throw new IllegalArgumentException();
}
mConfidence = confidence;
}
/**
* Returns maximum allowed number of iterations. If maximum allowed number
* of iterations is achieved without converting to a result when calling
* estimate(), a RobustEstimatorException will be raised.
*
* @return maximum allowed number of iterations.
*/
public int getMaxIterations() {
return mMaxIterations;
}
/**
* Sets maximum allowed number of iterations. When the maximum number of
* iterations is exceeded, result will not be available, however an
* approximate result will be available for retrieval.
*
* @param maxIterations maximum allowed number of iterations to be set.
* @throws IllegalArgumentException if provided value is less than 1.
* @throws LockedException if this estimator is locked because an estimation
* is being computed.
*/
public void setMaxIterations(final int maxIterations)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (maxIterations < MIN_ITERATIONS) {
throw new IllegalArgumentException();
}
mMaxIterations = maxIterations;
}
/**
* Gets data related to inliers found after estimation.
*
* @return data related to inliers found after estimation.
*/
public InliersData getInliersData() {
return mInliersData;
}
/**
* Indicates whether result must be refined using Levenberg-Marquardt
* fitting algorithm over found inliers.
* If true, inliers will be computed and kept in any implementation
* regardless of the settings.
*
* @return true to refine result, false to simply use result found by
* robust estimator without further refining.
*/
public boolean isResultRefined() {
return mRefineResult;
}
/**
* Specifies whether result must be refined using Levenberg-Marquardt
* fitting algorithm over found inliers.
*
* @param refineResult true to refine result, false to simply use result
* found by robust estimator without further refining.
* @throws LockedException if estimator is locked.
*/
public void setResultRefined(final boolean refineResult) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mRefineResult = refineResult;
}
/**
* Indicates whether covariance must be kept after refining result.
* This setting is only taken into account if result is refined.
*
* @return true if covariance must be kept after refining result, false
* otherwise.
*/
public boolean isCovarianceKept() {
return mKeepCovariance;
}
/**
* Specifies whether covariance must be kept after refining result.
* This setting is only taken into account if result is refined.
*
* @param keepCovariance true if covariance must be kept after refining
* result, false otherwise.
* @throws LockedException if estimator is locked.
*/
public void setCovarianceKept(final boolean keepCovariance)
throws LockedException {
if (isLocked()) {
throw new LockedException();
}
mKeepCovariance = keepCovariance;
}
/**
* Returns minimum number of matched pair of points required to start
* the estimation.
*
* @return minimum number of matched pair of points required to start
* the estimation.
*/
public int getMinRequiredPoints() {
return mFundMatrixEstimator.getMinRequiredPoints();
}
/**
* Returns value indicating whether required data has been provided so that
* fundamental matrix estimation can start.
* If true, estimator is ready to compute a fundamental matrix, otherwise
* more data needs to be provided.
*
* @return true if estimator is ready, false otherwise.
*/
public boolean isReady() {
return mLeftPoints != null && mRightPoints != null &&
mLeftPoints.size() == mRightPoints.size() &&
mLeftPoints.size() >= SevenPointsFundamentalMatrixEstimator.MIN_REQUIRED_POINTS;
}
/**
* Returns quality scores corresponding to each pair of matched points.
* The larger the score value the better the quality of the pair of matched
* points.
* This implementation always returns null.
* Subclasses using quality scores must implement proper behaviour.
*
* @return quality scores corresponding to each pair of matched points.
*/
public double[] getQualityScores() {
return null;
}
/**
* Sets quality scores corresponding to each pair of matched points.
* The larger the score value the better the quality of the pair of matched
* points.
* This implementation makes no action.
* Subclasses using quality scores must implement proper behaviour.
*
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws IllegalArgumentException if provided quality scores length is
* smaller than minimum required number of homographies.
*/
public void setQualityScores(final double[] qualityScores) throws LockedException {
}
/**
* Gets estimated covariance of estimated fundamental matrix if available.
* This is only available when result has been refined and covariance is
* kept.
*
* @return estimated covariance or null.
*/
public Matrix getCovariance() {
return mCovariance;
}
/**
* Estimates fundamental matrix.
*
* @return estimated fundamental matrix.
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress.
* @throws NotReadyException if provided input data is not enough to start
* the estimation.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
public abstract FundamentalMatrix estimate() throws LockedException,
NotReadyException, RobustEstimatorException;
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
public abstract RobustEstimatorMethod getMethod();
/**
* Creates a fundamental matrix robust estimator using provided method.
*
* @param method method of a robust estimator algorithm to estimate best
* fundamental matrix.
* @return an instance of a fundamental matrix robust estimator.
*/
public static FundamentalMatrixRobustEstimator create(
final RobustEstimatorMethod method) {
switch (method) {
case LMedS:
return new LMedSFundamentalMatrixRobustEstimator();
case MSAC:
return new MSACFundamentalMatrixRobustEstimator();
case PROSAC:
return new PROSACFundamentalMatrixRobustEstimator();
case PROMedS:
return new PROMedSFundamentalMatrixRobustEstimator();
case RANSAC:
default:
return new RANSACFundamentalMatrixRobustEstimator();
}
}
/**
* Creates a fundamental matrix robust estimator using provided lists of
* matched points and provided method.
*
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on left view.
* @param method method of a robust estimator algorithm to estimate best
* fundamental matrix.
* @return an instance of a fundamental matrix robust estimator.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public static FundamentalMatrixRobustEstimator create(
final List leftPoints, final List rightPoints,
final RobustEstimatorMethod method) {
switch (method) {
case LMedS:
return new LMedSFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
case MSAC:
return new MSACFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
case PROSAC:
return new PROSACFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
case PROMedS:
return new PROMedSFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
case RANSAC:
default:
return new RANSACFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
}
}
/**
* Creates a fundamental matrix robust estimator using provided lists of
* matched points and provided method.
*
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on left view.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @param method method of a robust estimator algorithm to estimate best
* fundamental matrix.
* @return an instance of a fundamental matrix robust estimator.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public static FundamentalMatrixRobustEstimator create(
final List leftPoints, final List rightPoints,
final double[] qualityScores, final RobustEstimatorMethod method) {
switch (method) {
case LMedS:
return new LMedSFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
case MSAC:
return new MSACFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
case PROSAC:
return new PROSACFundamentalMatrixRobustEstimator(qualityScores,
leftPoints, rightPoints);
case PROMedS:
return new PROMedSFundamentalMatrixRobustEstimator(
qualityScores, leftPoints, rightPoints);
case RANSAC:
default:
return new RANSACFundamentalMatrixRobustEstimator(leftPoints,
rightPoints);
}
}
/**
* Creates a fundamental matrix robust estimator using default method.
*
* @return an instance of a fundamental matrix robust estimator.
*/
public static FundamentalMatrixRobustEstimator create() {
return create(DEFAULT_ROBUST_METHOD);
}
/**
* Creates a fundamental matrix robust estimator using provided lists of
* matched points and default method.
*
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on left view.
* @return an instance of a fundamental matrix robust estimator.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public static FundamentalMatrixRobustEstimator create(
final List leftPoints, final List rightPoints) {
return create(leftPoints, rightPoints, DEFAULT_ROBUST_METHOD);
}
/**
* Creates a fundamental matrix robust estimator using provided lists of
* matched points and default method.
*
* @param leftPoints 2D points on left view.
* @param rightPoints 2D points on left view.
* @param qualityScores quality scores corresponding to each pair of matched
* points.
* @return an instance of a fundamental matrix robust estimator.
* @throws IllegalArgumentException if provided list of points do not have
* the same length or their length is less than 7 points.
*/
public static FundamentalMatrixRobustEstimator create(
final List leftPoints, final List rightPoints,
final double[] qualityScores) {
return create(leftPoints, rightPoints, qualityScores,
DEFAULT_ROBUST_METHOD);
}
/**
* Computes the residual between a fundamental matrix and a pair of matched
* points.
*
* @param fundamentalMatrix a fundamental matrix.
* @param leftPoint left 2D point.
* @param rightPoint right 2D point.
* @return residual (distance of point to epipolar line).
*/
@SuppressWarnings("DuplicatedCode")
protected double residual(final FundamentalMatrix fundamentalMatrix,
final Point2D leftPoint, final Point2D rightPoint) {
try {
leftPoint.normalize();
rightPoint.normalize();
fundamentalMatrix.normalize();
fundamentalMatrix.leftEpipolarLine(rightPoint, mTestLine);
final double leftDistance = Math.abs(mTestLine.signedDistance(
leftPoint));
fundamentalMatrix.rightEpipolarLine(leftPoint, mTestLine);
final double rightDistance = Math.abs(mTestLine.signedDistance(
rightPoint));
// return average distance as an error residual
return 0.5 * (leftDistance + rightDistance);
} catch (final NotReadyException e) {
return Double.MAX_VALUE;
}
}
/**
* Estimates a fundamental matrix using a non robust method and provided
* subset of matched points and stores the solution in provided array of
* solutions.
*
* @param solutions list where solutions will be stored.
* @param subsetLeftPoints subset of left view matched points.
* @param subsetRightPoints subset of right view matched points.
*/
protected void nonRobustEstimate(final List solutions,
final List subsetLeftPoints,
final List subsetRightPoints) {
try {
mFundMatrixEstimator.setPoints(subsetLeftPoints,
subsetRightPoints);
if (mFundMatrixEstimator.getMethod() ==
FundamentalMatrixEstimatorMethod.SEVEN_POINTS_ALGORITHM) {
final List matrices =
((SevenPointsFundamentalMatrixEstimator)
mFundMatrixEstimator).estimateAll();
solutions.addAll(matrices);
} else {
solutions.add(mFundMatrixEstimator.estimate());
}
} catch (final GeometryException e) {
// if anything fails, no solution is added
}
}
/**
* Attempts to refine provided solution if refinement is requested.
* This method returns a refined solution or the same provided solution
* if refinement is not requested or has failed.
* If refinement is enabled and it is requested to keep covariance, this
* method will also keep covariance of refined fundamental matrix.
*
* @param fundamentalMatrix fundamental matrix estimated by a robust
* estimator without refinement.
* @return solution after refinement (if requested) or the provided non-
* refined solution if not requested or if refinement failed.
*/
protected FundamentalMatrix attemptRefine(
final FundamentalMatrix fundamentalMatrix) {
if (mRefineResult) {
final FundamentalMatrixRefiner refiner = new FundamentalMatrixRefiner(
fundamentalMatrix, mKeepCovariance, getInliersData(),
mLeftPoints, mRightPoints,
getRefinementStandardDeviation());
try {
final FundamentalMatrix result = new FundamentalMatrix();
final boolean improved = refiner.refine(result);
if (mKeepCovariance) {
// keep covariance
mCovariance = refiner.getCovariance();
}
return improved ? result : fundamentalMatrix;
} catch (final Exception e) {
// refinement failed, so we return input value
return fundamentalMatrix;
}
} else {
return fundamentalMatrix;
}
}
/**
* Gets standard deviation used for Levenberg-Marquardt fitting during
* refinement.
* Returned value gives an indication of how much variance each residual
* has.
* Typically this value is related to the threshold used on each robust
* estimation, since residuals of found inliers are within the range of
* such threshold.
*
* @return standard deviation used for refinement.
*/
protected abstract double getRefinementStandardDeviation();
/**
* Sets matched 2D points on left and right views.
* This method does not check whether instance is locked or not.
*
* @param leftPoints matched 2D points on left view.
* @param rightPoints matched 2D points on right view.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size.
*/
private void internalSetPoints(final List leftPoints,
final List rightPoints) {
if (leftPoints.size() != rightPoints.size()) {
throw new IllegalArgumentException();
}
if (leftPoints.size() < getMinRequiredPoints()) {
throw new IllegalArgumentException();
}
mLeftPoints = leftPoints;
mRightPoints = rightPoints;
}
}
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