LMedSPoint3DRobustEstimator.java
/*
* Copyright (C) 2015 Alberto Irurueta Carro (alberto@irurueta.com)
*
* 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.geometry.estimators;
import com.irurueta.geometry.NoIntersectionException;
import com.irurueta.geometry.Plane;
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.List;
/**
* Finds the best 3D point for provided collection of 3D planes using LMedS
* algorithm
*/
public class LMedSPoint3DRobustEstimator extends Point3DRobustEstimator {
/**
* Default value to 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 = 1e-3;
/**
* 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 LMedSPoint3DRobustEstimator() {
super();
stopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor with planes.
*
* @param planes 3D planes to estimate a 3D point.
* @throws IllegalArgumentException if provided list of planes doesn't have
* a size greater or equal than MINIMUM_SIZE.
*/
public LMedSPoint3DRobustEstimator(final List<Plane> planes) {
super(planes);
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 LMedSPoint3DRobustEstimator(final Point3DRobustEstimatorListener listener) {
super(listener);
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 planes 3D planes to estimate a 3D point.
* @throws IllegalArgumentException if provided list of planes doesn't have
* a size greater or equal than MINIMUM_SIZE.
*/
public LMedSPoint3DRobustEstimator(final Point3DRobustEstimatorListener listener, final List<Plane> planes) {
super(listener, planes);
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 3D point using a robust estimator and the best set of 3D
* planes that intersect into the estimated 3D point.
*
* @return a 3D point.
* @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 Point3D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final var innerEstimator = new LMedSRobustEstimator<>(new LMedSRobustEstimatorListener<Point3D>() {
@Override
public int getTotalSamples() {
return planes.size();
}
@Override
public int getSubsetSize() {
return Point3DRobustEstimator.MINIMUM_SIZE;
}
@SuppressWarnings("DuplicatedCode")
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices, final List<Point3D> solutions) {
final var plane1 = planes.get(samplesIndices[0]);
final var plane2 = planes.get(samplesIndices[1]);
final var plane3 = planes.get(samplesIndices[2]);
try {
final var point = plane1.getIntersection(plane2, plane3);
solutions.add(point);
} catch (final NoIntersectionException e) {
// if points are coincident, no solution is added
}
}
@Override
public double computeResidual(final Point3D currentEstimation, final int i) {
return residual(currentEstimation, planes.get(i));
}
@Override
public boolean isReady() {
return LMedSPoint3DRobustEstimator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator<Point3D> estimator) {
if (listener != null) {
listener.onEstimateStart(LMedSPoint3DRobustEstimator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator<Point3D> estimator) {
if (listener != null) {
listener.onEstimateEnd(LMedSPoint3DRobustEstimator.this);
}
}
@Override
public void onEstimateNextIteration(final RobustEstimator<Point3D> estimator, final int iteration) {
if (listener != null) {
listener.onEstimateNextIteration(LMedSPoint3DRobustEstimator.this, iteration);
}
}
@Override
public void onEstimateProgressChange(final RobustEstimator<Point3D> estimator, final float progress) {
if (listener != null) {
listener.onEstimateProgressChange(LMedSPoint3DRobustEstimator.this, progress);
}
}
});
try {
locked = true;
inliersData = null;
innerEstimator.setConfidence(confidence);
innerEstimator.setMaxIterations(maxIterations);
innerEstimator.setProgressDelta(progressDelta);
innerEstimator.setStopThreshold(stopThreshold);
final var result = innerEstimator.estimate();
inliersData = innerEstimator.getInliersData();
return attemptRefine(result);
} 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();
}
}