LMedSMetricTransformation3DRobustEstimator.java
/*
* @file
* This file contains implementation of
* com.irurueta.geometry.estimators.LMedSMetricTransformation3DRobustEstimator
*
* @author Alberto Irurueta (alberto@irurueta.com)
* @date March 24, 2017.
*/
package com.irurueta.geometry.estimators;
import com.irurueta.geometry.CoordinatesType;
import com.irurueta.geometry.MetricTransformation3D;
import com.irurueta.geometry.Point3D;
import com.irurueta.numerical.robust.LMedSRobustEstimator;
import com.irurueta.numerical.robust.LMedSRobustEstimatorListener;
import com.irurueta.numerical.robust.RobustEstimator;
import com.irurueta.numerical.robust.RobustEstimatorException;
import com.irurueta.numerical.robust.RobustEstimatorMethod;
import java.util.ArrayList;
import java.util.List;
/**
* Finds the best metric 3D transformation for provided collections of
* matched 3D points using LMedS algorithm.
*/
public class LMedSMetricTransformation3DRobustEstimator extends MetricTransformation3DRobustEstimator {
/**
* Default value ot be used for stop threshold. Stop threshold can be used
* to keep the algorithm iterating in case that best estimated threshold
* using median of residuals is not small enough. Once a solution is found
* that generates a threshold below this value, the algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
public static final double DEFAULT_STOP_THRESHOLD = 1.0;
/**
* Minimum allowed stop threshold value.
*/
public static final double MIN_STOP_THRESHOLD = 0.0;
/**
* Threshold to be used to keep the algorithm iterating in case that best
* estimated threshold using median of residuals is not small enough. Once
* a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*/
private double stopThreshold;
/**
* Constructor.
*/
public LMedSMetricTransformation3DRobustEstimator() {
super();
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with lists of points to be used to estimate a metric 3D
* transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputPoints list of input points to be used to estimate a
* metric 3D transformation.
* @param outputPoints list of output points to be used to estimate a
* metric 3D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public LMedSMetricTransformation3DRobustEstimator(
final List<Point3D> inputPoints, final List<Point3D> outputPoints) {
super(inputPoints, outputPoints);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public LMedSMetricTransformation3DRobustEstimator(final MetricTransformation3DRobustEstimatorListener listener) {
super(listener);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with listener and lists of points to be used to estimate a
* metric 2D transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate a
* metric 3D transformation.
* @param outputPoints list of output points to be used to estimate a
* metric 3D transformation.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public LMedSMetricTransformation3DRobustEstimator(
final MetricTransformation3DRobustEstimatorListener listener,
final List<Point3D> inputPoints, final List<Point3D> outputPoints) {
super(listener, inputPoints, outputPoints);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
*/
public LMedSMetricTransformation3DRobustEstimator(final boolean weakMinimumSizeAllowed) {
super(weakMinimumSizeAllowed);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with lists of points to be used to estimate a metric 3D
* transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param inputPoints list of input points to be used to estimate a
* metric 3D transformation.
* @param outputPoints list of output points to be used to estimate a
* metric 3D transformation.
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public LMedSMetricTransformation3DRobustEstimator(
final List<Point3D> inputPoints, final List<Point3D> outputPoints, final boolean weakMinimumSizeAllowed) {
super(inputPoints, outputPoints, weakMinimumSizeAllowed);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
*/
public LMedSMetricTransformation3DRobustEstimator(
final MetricTransformation3DRobustEstimatorListener listener, final boolean weakMinimumSizeAllowed) {
super(listener, weakMinimumSizeAllowed);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with listener and lists of points to be used to estimate a
* metric 2D transformation.
* Points in the list located at the same position are considered to be
* matched. Hence, both lists must have the same size, and their size must
* be greater or equal than MINIMUM_SIZE.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @param inputPoints list of input points to be used to estimate a
* metric 3D transformation.
* @param outputPoints list of output points to be used to estimate a
* metric 3D transformation.
* @param weakMinimumSizeAllowed true allows 3 points, false requires 4.
* @throws IllegalArgumentException if provided lists of points don't have
* the same size or their size is smaller than MINIMUM_SIZE.
*/
public LMedSMetricTransformation3DRobustEstimator(
final MetricTransformation3DRobustEstimatorListener listener,
final List<Point3D> inputPoints, List<Point3D> outputPoints, final boolean weakMinimumSizeAllowed) {
super(listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Returns threshold to be used to keep the algorithm iterating in case that
* best estimated threshold using median of residuals is not small enough.
* Once a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results.
*
* @return stop threshold to stop the algorithm prematurely when a certain
* accuracy has been reached.
*/
public double getStopThreshold() {
return stopThreshold;
}
/**
* Sets threshold to be used to keep the algorithm iterating in case that
* best estimated threshold using median of residuals is not small enough.
* Once a solution is found that generates a threshold below this value, the
* algorithm will stop.
* The stop threshold can be used to prevent the LMedS algorithm iterating
* too many times in cases where samples have a very similar accuracy.
* For instance, in cases where proportion of outliers is very small (close
* to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
* iterate for a long time trying to find the best solution when indeed
* there is no need to do that if a reasonable threshold has already been
* reached.
* Because of this behaviour the stop threshold can be set to a value much
* lower than the one typically used in RANSAC, and yet the algorithm could
* still produce even smaller thresholds in estimated results
*
* @param stopThreshold stop threshold to stop the algorithm prematurely
* when a certain accuracy has been reached
* @throws IllegalArgumentException if provided value is zero or negative
* @throws LockedException if robust estimator is locked because an
* estimation is already in progress
*/
public void setStopThreshold(final double stopThreshold) throws LockedException {
if (isLocked()) {
throw new LockedException();
}
if (stopThreshold <= MIN_STOP_THRESHOLD) {
throw new IllegalArgumentException();
}
this.stopThreshold = stopThreshold;
}
/**
* Estimates a metric 3D transformation using a robust estimator and
* the best set of matched 3D point correspondences found using the robust
* estimator.
*
* @return a metric 3D transformation.
* @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).
*/
@Override
public MetricTransformation3D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new LMedSRobustEstimator<>(
new LMedSRobustEstimatorListener<MetricTransformation3D>() {
// point to be reused when computing residuals
private final Point3D testPoint = Point3D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
private final MetricTransformation3DEstimator nonRobustEstimator =
new MetricTransformation3DEstimator(isWeakMinimumSizeAllowed());
private final List<Point3D> subsetInputPoints = new ArrayList<>();
private final List<Point3D> subsetOutputPoints = new ArrayList<>();
@Override
public int getTotalSamples() {
return inputPoints.size();
}
@Override
public int getSubsetSize() {
return nonRobustEstimator.getMinimumPoints();
}
@SuppressWarnings("DuplicatedCode")
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices,
final List<MetricTransformation3D> solutions) {
subsetInputPoints.clear();
subsetOutputPoints.clear();
for (final var samplesIndex : samplesIndices) {
subsetInputPoints.add(inputPoints.get(samplesIndex));
subsetOutputPoints.add(outputPoints.get(
samplesIndex));
}
try {
nonRobustEstimator.setPoints(subsetInputPoints, subsetOutputPoints);
solutions.add(nonRobustEstimator.estimate());
} catch (final Exception e) {
// if points are coincident, no solution is added
}
}
@Override
public double computeResidual(final MetricTransformation3D currentEstimation, final int i) {
final var inputPoint = inputPoints.get(i);
final var outputPoint = outputPoints.get(i);
// transform input point and store result in mTestPoint
currentEstimation.transform(inputPoint, testPoint);
return outputPoint.distanceTo(testPoint);
}
@Override
public boolean isReady() {
return LMedSMetricTransformation3DRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<MetricTransformation3D> estimator) {
if (listener != null) {
listener.onEstimateStart(LMedSMetricTransformation3DRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<MetricTransformation3D> estimator) {
if (listener != null) {
listener.onEstimateEnd(LMedSMetricTransformation3DRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator<MetricTransformation3D> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(
LMedSMetricTransformation3DRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator<MetricTransformation3D> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(
LMedSMetricTransformation3DRobustEstimator.this, progress);
}
}
});
try {
locked = true;
inliersData = null;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
innerEstimator.setStopThreshold(stopThreshold);
final var transformation = innerEstimator.estimate();
inliersData = innerEstimator.getInliersData();
return attemptRefine(transformation);
} catch (final com.irurueta.numerical.LockedException e) {
throw new LockedException(e);
} catch (final com.irurueta.numerical.NotReadyException e) {
throw new NotReadyException(e);
} finally {
locked = false;
}
}
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
@Override
public RobustEstimatorMethod getMethod() {
return RobustEstimatorMethod.LMEDS;
}
/**
* 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.
*/
@Override
protected double getRefinementStandardDeviation() {
final var inliersData = (LMedSRobustEstimator.LMedSInliersData) getInliersData();
return inliersData.getEstimatedThreshold();
}
}