KnownBaselineSparseReconstructor.java
/*
* Copyright (C) 2017 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.ar.sfm;
import com.irurueta.geometry.MetricTransformation3D;
import com.irurueta.geometry.Point3D;
import java.util.ArrayList;
/**
* Class in charge of estimating cameras and 3D reconstructed points from sparse
* image point correspondences from multiple views and known initial camera baseline
* (camera separation), so that cameras and reconstructed points are obtained with
* exact scale.
*/
public class KnownBaselineSparseReconstructor extends
BaseSparseReconstructor<KnownBaselineSparseReconstructorConfiguration, KnownBaselineSparseReconstructor,
KnownBaselineSparseReconstructorListener> {
/**
* Constructor.
*
* @param configuration configuration for this re-constructor.
* @param listener listener in charge of handling events.
* @throws NullPointerException if listener or configuration is not
* provided.
*/
public KnownBaselineSparseReconstructor(
final KnownBaselineSparseReconstructorConfiguration configuration,
final KnownBaselineSparseReconstructorListener listener) {
super(configuration, listener);
}
/**
* Constructor with default configuration.
*
* @param listener listener in charge of handling events.
* @throws NullPointerException if listener is not provided.
*/
public KnownBaselineSparseReconstructor(final KnownBaselineSparseReconstructorListener listener) {
this(new KnownBaselineSparseReconstructorConfiguration(), listener);
}
/**
* Called when processing one frame is successfully finished. This can be done to estimate scale on
* those implementations where scale can be measured or is already known.
*
* @param isInitialPairOfViews true if initial pair of views is being processed, false otherwise.
* @return true if post-processing succeeded, false otherwise.
*/
@SuppressWarnings("DuplicatedCode")
@Override
protected boolean postProcessOne(final boolean isInitialPairOfViews) {
try {
final var metricCamera1 = previousMetricEstimatedCamera.getCamera();
final var metricCamera2 = currentMetricEstimatedCamera.getCamera();
metricCamera1.decompose();
metricCamera2.decompose();
final double scale;
if (isInitialPairOfViews) {
// reconstruction succeeded, so we update scale of cameras and
// reconstructed points
final var baseline = configuration.getBaseline();
final var center1 = metricCamera1.getCameraCenter();
final var center2 = metricCamera2.getCameraCenter();
final var estimatedBaseline = center1.distanceTo(center2);
scale = currentScale = baseline / estimatedBaseline;
} else {
scale = currentScale;
}
final var sqrScale = scale * scale;
final var scaleTransformation = new MetricTransformation3D(scale);
// update scale of cameras
final var euclideanCamera1 = scaleTransformation.transformAndReturnNew(metricCamera1);
final var euclideanCamera2 = scaleTransformation.transformAndReturnNew(metricCamera2);
previousEuclideanEstimatedCamera = new EstimatedCamera();
previousEuclideanEstimatedCamera.setCamera(euclideanCamera1);
previousEuclideanEstimatedCamera.setViewId(previousMetricEstimatedCamera.getViewId());
previousEuclideanEstimatedCamera.setQualityScore(previousMetricEstimatedCamera.getQualityScore());
if (previousMetricEstimatedCamera.getCovariance() != null) {
previousEuclideanEstimatedCamera.setCovariance(previousMetricEstimatedCamera.getCovariance()
.multiplyByScalarAndReturnNew(sqrScale));
}
currentEuclideanEstimatedCamera = new EstimatedCamera();
currentEuclideanEstimatedCamera.setCamera(euclideanCamera2);
currentEuclideanEstimatedCamera.setViewId(currentMetricEstimatedCamera.getViewId());
currentEuclideanEstimatedCamera.setQualityScore(currentMetricEstimatedCamera.getQualityScore());
if (currentMetricEstimatedCamera.getCovariance() != null) {
currentEuclideanEstimatedCamera.setCovariance(
currentMetricEstimatedCamera.getCovariance().multiplyByScalarAndReturnNew(sqrScale));
}
// update scale of reconstructed points
final var numPoints = activeMetricReconstructedPoints.size();
final var metricReconstructedPoints3D = new ArrayList<Point3D>();
for (final var activeMetricReconstructedPoint : activeMetricReconstructedPoints) {
metricReconstructedPoints3D.add(activeMetricReconstructedPoint.getPoint());
}
final var euclideanReconstructedPoints3D = scaleTransformation.transformPointsAndReturnNew(
metricReconstructedPoints3D);
// set scaled points into result
activeEuclideanReconstructedPoints = new ArrayList<>();
ReconstructedPoint3D euclideanPoint;
ReconstructedPoint3D metricPoint;
for (var i = 0; i < numPoints; i++) {
metricPoint = activeMetricReconstructedPoints.get(i);
euclideanPoint = new ReconstructedPoint3D();
euclideanPoint.setId(metricPoint.getId());
euclideanPoint.setPoint(euclideanReconstructedPoints3D.get(i));
euclideanPoint.setInlier(metricPoint.isInlier());
euclideanPoint.setQualityScore(metricPoint.getQualityScore());
if (metricPoint.getCovariance() != null) {
euclideanPoint.setCovariance(metricPoint.getCovariance().multiplyByScalarAndReturnNew(sqrScale));
}
euclideanPoint.setColorData(metricPoint.getColorData());
activeEuclideanReconstructedPoints.add(euclideanPoint);
}
return true;
} catch (final Exception e) {
failed = true;
listener.onFail(this);
return false;
}
}
}