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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.sfm;
import com.irurueta.geometry.CoordinatesType;
import com.irurueta.geometry.PinholeCamera;
import com.irurueta.geometry.Point2D;
import com.irurueta.geometry.Point3D;
import com.irurueta.geometry.estimators.LockedException;
import com.irurueta.geometry.estimators.NotReadyException;
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;
/**
* Robustly triangulates 3D points from matched 2D points and their
* corresponding cameras on several views using LMedS algorithm.
*/
public class LMedSRobustSinglePoint3DTriangulator extends
RobustSinglePoint3DTriangulator {
/**
* 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 mStopThreshold;
/**
* Constructor.
*/
public LMedSRobustSinglePoint3DTriangulator() {
super();
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
*/
public LMedSRobustSinglePoint3DTriangulator(
final RobustSinglePoint3DTriangulatorListener listener) {
super(listener);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param points Matched 2D points. Each point in the list is assumed to be
* projected by the corresponding camera in the list.
* @param cameras List of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
public LMedSRobustSinglePoint3DTriangulator(final List points,
final List cameras) {
super(points, cameras);
mStopThreshold = DEFAULT_STOP_THRESHOLD;
}
/**
* Constructor.
*
* @param points Matched 2D points. Each point in the list is assumed to be
* projected by the corresponding camera in the list.
* @param cameras List of cameras associated to the matched 2D point on the
* same position as the camera on the list.
* @param listener listener to be notified of events such as when estimation
* starts, ends or its progress significantly changes.
* @throws IllegalArgumentException if provided lists don't have the same
* length or their length is less than 2 views, which is the minimum
* required to compute triangulation.
*/
public LMedSRobustSinglePoint3DTriangulator(final List points,
final List cameras,
final RobustSinglePoint3DTriangulatorListener listener) {
super(points, cameras, listener);
mStopThreshold = 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 mStopThreshold;
}
/**
* 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();
}
mStopThreshold = stopThreshold;
}
/**
* Triangulates provided matched 2D points being projected by each
* corresponding camera into a single 3D point.
* At least 2 matched 2D points and their corresponding 2 cameras are
* required to compute triangulation. If more views are provided, an
* averaged solution can be found.
*
* @return computed triangulated 3D point.
* @throws LockedException if this instance is locked.
* @throws NotReadyException if lists of points and cameras don't have the
* same length or less than 2 views are provided.
* @throws RobustEstimatorException if estimation fails for any reason
* (i.e. numerical instability, no solution available, etc).
*/
@SuppressWarnings("DuplicatedCode")
@Override
public Point3D triangulate() throws LockedException, NotReadyException,
RobustEstimatorException {
if (isLocked()) {
throw new LockedException();
}
if (!isReady()) {
throw new NotReadyException();
}
final LMedSRobustEstimator innerEstimator =
new LMedSRobustEstimator<>(
new LMedSRobustEstimatorListener() {
// point to be reused when computing residuals
private final Point2D mTestPoint = Point2D.create(
CoordinatesType.HOMOGENEOUS_COORDINATES);
// non-robust 3D point triangulator
private final SinglePoint3DTriangulator mTriangulator =
SinglePoint3DTriangulator.create(mUseHomogeneousSolution ?
Point3DTriangulatorType.LMSE_HOMOGENEOUS_TRIANGULATOR :
Point3DTriangulatorType.LMSE_INHOMOGENEOUS_TRIANGULATOR);
// subset of 2D points
private final List mSubsetPoints = new ArrayList<>();
// subst of cameras
private final List mSubsetCameras =
new ArrayList<>();
@Override
public int getTotalSamples() {
return mPoints2D.size();
}
@Override
public int getSubsetSize() {
return MIN_REQUIRED_VIEWS;
}
@Override
public void estimatePreliminarSolutions(final int[] samplesIndices,
final List solutions) {
mSubsetPoints.clear();
mSubsetPoints.add(mPoints2D.get(samplesIndices[0]));
mSubsetPoints.add(mPoints2D.get(samplesIndices[1]));
mSubsetCameras.clear();
mSubsetCameras.add(mCameras.get(samplesIndices[0]));
mSubsetCameras.add(mCameras.get(samplesIndices[1]));
try {
mTriangulator.setPointsAndCameras(mSubsetPoints,
mSubsetCameras);
final Point3D triangulated = mTriangulator.triangulate();
solutions.add(triangulated);
} catch (final Exception e) {
// if anything fails, no solution is added
}
}
@Override
public double computeResidual(final Point3D currentEstimation, final int i) {
final Point2D point2D = mPoints2D.get(i);
final PinholeCamera camera = mCameras.get(i);
// project estimated point with camera
camera.project(currentEstimation, mTestPoint);
// return distance of projected point respect to the original one
// as a residual
return mTestPoint.distanceTo(point2D);
}
@Override
public boolean isReady() {
return LMedSRobustSinglePoint3DTriangulator.this.isReady();
}
@Override
public void onEstimateStart(final RobustEstimator estimator) {
if (mListener != null) {
mListener.onTriangulateStart(
LMedSRobustSinglePoint3DTriangulator.this);
}
}
@Override
public void onEstimateEnd(final RobustEstimator estimator) {
if (mListener != null) {
mListener.onTriangulateEnd(
LMedSRobustSinglePoint3DTriangulator.this);
}
}
@Override
public void onEstimateNextIteration(
final RobustEstimator estimator, final int iteration) {
if (mListener != null) {
mListener.onTriangulateNextIteration(
LMedSRobustSinglePoint3DTriangulator.this,
iteration);
}
}
@Override
public void onEstimateProgressChange(
final RobustEstimator estimator, final float progress) {
if (mListener != null) {
mListener.onTriangulateProgressChange(
LMedSRobustSinglePoint3DTriangulator.this,
progress);
}
}
});
try {
mLocked = true;
innerEstimator.setConfidence(mConfidence);
innerEstimator.setMaxIterations(mMaxIterations);
innerEstimator.setProgressDelta(mProgressDelta);
innerEstimator.setStopThreshold(mStopThreshold);
return innerEstimator.estimate();
} catch (final com.irurueta.numerical.LockedException e) {
throw new LockedException(e);
} catch (final com.irurueta.numerical.NotReadyException e) {
throw new NotReadyException(e);
} finally {
mLocked = false;
}
}
/**
* Returns method being used for robust estimation.
*
* @return method being used for robust estimation.
*/
@Override
public RobustEstimatorMethod getMethod() {
return RobustEstimatorMethod.LMedS;
}
}
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