Example Multiview Uncalibrated Reconstruction Sparse

From BoofCV
Snow tree.jpg
Scene being reconstructed Pseudo color sparse reconstruction from uncalibrated input images.

Reconstruction from uncalibrated images is one of the more challenging problems in 3D scene reconstruction since the lens parameters are not initially known and are notoriously unstable to estimate. In this example BoofCV takes a sequence of uncalibrated images and carefully estimates the metric reconstruction from the sparse features. This will then be used to perform dense reconstruction.

Example Code:


Example Code

 * Estimate scene parameters using a sparse set of features across uncalibrated images. In this example, a KLT
 * feature tracker will be used due to speed and simplicity even though there are some disadvantages
 * mentioned below. After image features have been tracked across the sequence we will first determine 3D
 * connectivity through two-view geometry, followed my a metric elevation. Then a final refinement
 * using bundle adjustment.
 * This is unusual in that it will estimate intrinsic parameters from scratch with very few assumptions.
 * Most MVS software uses a data base of known camera parameters to provide an initial seed as this can simplify
 * the problem and make it more stable.
 * @author Peter Abeles
public class ExampleMultiViewSparseReconstruction {

	// Instead of processing all the frames just process the first N frames
	int maxFrames = 40;

	String workDirectory;
	List<String> imageFiles = new ArrayList<>();

	PairwiseImageGraph pairwise = null;
	LookUpSimilarImages similarImages;
	SceneWorkingGraph working = null;
	SceneStructureMetric scene = null;

	boolean rebuild = false;

	public static void main( String[] args ) {
		var example = new ExampleMultiViewSparseReconstruction();
//		example.maxFrames = 100;       // This will process the entire sequence
//		example.compute("ditch_02.mp4");
//		example.compute("holiday_display_01.mp4");
//		example.compute("log_building_02.mp4");


	public void compute( String videoName ) {
		// Turn on threaded code for bundle adjustment
		DDoglegConcurrency.USE_CONCURRENT = true;

		// Create a directory to store the work space
		String path = UtilIO.pathExample("mvs/" + videoName);
		workDirectory = "mvs_work/" + FilenameUtils.getBaseName(videoName);

		// Convert the video into an image sequence. Later on we will need to access the images in random order
		var imageDirectory = new File(workDirectory, "images");
		if (!imageDirectory.exists())
			checkTrue(imageDirectory.mkdirs(), "Failed to image directory");
		SimpleImageSequence<InterleavedU8> sequence = DefaultMediaManager.INSTANCE.openVideo(path, ImageType.IL_U8);
		System.out.println("### Decoding Video");
		BoofMiscOps.profile(() -> {
			int frame = 0;
			while (sequence.hasNext() && frame < maxFrames) {
				InterleavedU8 image = sequence.next();
				File imageFile = new File(imageDirectory, String.format("frame%d.png", frame++));
				// This is commented out for what appears to be a JRE bug.
				// V  [libjvm.so+0xdc4059]  SWPointer::SWPointer(MemNode*, SuperWord*, Node_Stack*, bool)
//				if (imageFile.exists())
//					continue;
				UtilImageIO.saveImage(image, imageFile.getPath());
		}, "Video Decoding");


		Rodrigues_F64 rod = new Rodrigues_F64();
		for (PairwiseImageGraph.View pv : pairwise.nodes.toList()) {
			var wv = working.lookupView(pv.id);
			if (wv == null)
			int order = working.viewList.indexOf(wv);
			ConvertRotation3D_F64.matrixToRodrigues(wv.world_to_view.R, rod);
			System.out.printf("view[%2d]='%2s' f=%6.1f k1=%6.3f k2=%6.3f T={%5.1f,%5.1f,%5.1f} R=%4.2f\n",
					order, wv.pview.id, wv.intrinsic.f, wv.intrinsic.k1, wv.intrinsic.k2,
					wv.world_to_view.T.x, wv.world_to_view.T.y, wv.world_to_view.T.z, rod.theta);
		System.out.println("   Views used: " + scene.views.size + " / " + pairwise.nodes.size);

	 * For a pairwise graph to be constructed, image feature relationships between frames are needed. For a video
	 * sequence, KLT is an easy and fast way to do this. However, KLT will not "close the loop", and it will
	 * not realize you're back at the initial location. Typically this results in a noticeable miss alignment.
	private void trackImageFeatures() {
		if (similarImages!=null)
		System.out.println("### Creating Similar Images");

		// Configure the KLT tracker
		int radius = 5;
		var configTracker = new ConfigPointTracker();
		configTracker.typeTracker = ConfigPointTracker.TrackerType.KLT;
		configTracker.klt.pruneClose = true;
		configTracker.klt.toleranceFB = 2;
		configTracker.klt.templateRadius = radius;
		configTracker.klt.config.maxIterations = 30;
		configTracker.detDesc.typeDetector = ConfigDetectInterestPoint.DetectorType.POINT;
		configTracker.detDesc.detectPoint.type = PointDetectorTypes.SHI_TOMASI;
		configTracker.detDesc.detectPoint.shiTomasi.radius = 6;
		configTracker.detDesc.detectPoint.general.radius = 4;
//		configTracker.detDesc.detectPoint.general.threshold = 0;
		configTracker.detDesc.detectPoint.general.selector = ConfigSelectLimit.selectUniform(2.0);

		PointTracker<GrayU8> tracker = FactoryPointTracker.tracker(configTracker, GrayU8.class, null);

		var trackerSimilar = new PointTrackerToSimilarImages();

		// Track features across the entire sequence and save the results
		BoofMiscOps.profile(() -> {
			boolean first = true;
			for (int frameId = 0; frameId < imageFiles.size(); frameId++) {
				String filePath = imageFiles.get(frameId);
				GrayU8 frame = UtilImageIO.loadImage(filePath, GrayU8.class);
				if (first) {
					first = false;
					trackerSimilar.initialize(frame.width, frame.height);
				int active = tracker.getTotalActive();
				int dropped = tracker.getDroppedTracks(null).size();
				String id = frameId + "";//trackerSimilar.frames.getTail().frameID;
				System.out.println("frame id = " + id + " active=" + active + " dropped=" + dropped);

				// TODO drop tracks which have been viewed for too long to reduce the negative affects of track drift?

				// To keep things manageable only process the first few frames, if configured to do so
				if (frameId >= maxFrames)
		}, "Tracking Features");

		similarImages = trackerSimilar;

	 * This step attempts to determine which views have a 3D (not homographic) relationship with each other and which
	 * features are real and not fake.
	public void computePairwiseGraph() {
		var savePath = new File(workDirectory, "pairwise.yaml");
		try {
			pairwise = MultiViewIO.load(savePath.getPath(), (PairwiseImageGraph)null);
		} catch (UncheckedIOException ignore) {}

		// Recompute if the number of images has changed
		if (!rebuild && pairwise != null && pairwise.nodes.size == imageFiles.size()) {
			System.out.println("Loaded Pairwise Graph");
		} else {
			rebuild = true;
			pairwise = null;

		System.out.println("### Creating Pairwise");
		var generatePairwise = new GeneratePairwiseImageGraph();
		BoofMiscOps.profile(() -> {
			generatePairwise.setVerbose(System.out, null);
		}, "Created Pairwise graph");
		pairwise = generatePairwise.getGraph();
		MultiViewIO.save(pairwise, savePath.getPath());
		System.out.println("  nodes.size=" + pairwise.nodes.size);
		System.out.println("  edges.size=" + pairwise.edges.size);

	 * Next a metric reconstruction is attempted using views with a 3D relationship. This is a tricky step
	 * and works by finding clusters of views which are likely to have numerically stable results then expanding
	 * the sparse metric reconstruction.
	public void metricFromPairwise() {
		var savePath = new File(workDirectory, "working.yaml");

		if (!rebuild) {
			try {
				working = MultiViewIO.load(savePath.getPath(), pairwise, null);
			} catch (UncheckedIOException ignore) {}

		// Recompute if the number of images has changed
		if (working != null){
			System.out.println("Loaded Metric Reconstruction");

		System.out.println("### Metric Reconstruction");

		var metric = new MetricFromUncalibratedPairwiseGraph();
		metric.setVerbose(System.out, null);
		metric.getInitProjective().setVerbose(System.out, null);
		metric.getExpandMetric().setVerbose(System.out, null);
		BoofMiscOps.profile(() -> {
			if (!metric.process(similarImages, pairwise)) {
				System.err.println("Reconstruction failed");
		}, "Metric Reconstruction");

		working = metric.getWorkGraph();
		MultiViewIO.save(working, savePath.getPath());

	 * Here the initial estimate found in the metric reconstruction is refined using Bundle Adjustment, which just
	 * means all parameters (camera, view pose, point location) are optimized all at once.
	public void bundleAdjustmentRefine() {
		var savePath = new File(workDirectory, "structure.yaml");

		if (!rebuild) {
			try {
				scene = MultiViewIO.load(savePath.getPath(), (SceneStructureMetric)null);
			} catch (UncheckedIOException ignore) {}
		// Recompute if the number of images has changed
		if (scene != null) {
			System.out.println("Loaded Refined Scene");

		System.out.println("Refining the scene");

		var refine = new RefineMetricWorkingGraph();
		BoofMiscOps.profile(() -> {
			// Bundle adjustment is run twice, with the worse 5% of points discarded in an attempt to reduce noise
			refine.bundleAdjustment.keepFraction = 0.95;
			refine.bundleAdjustment.getSba().setVerbose(System.out, null);
			if (!refine.process(similarImages, working)) {
				System.out.println("SBA REFINE FAILED");
		}, "Bundle Adjustment refine");
		scene = refine.bundleAdjustment.structure;
		MultiViewIO.save(scene, savePath.getPath());

	 * To visualize the results we will render a sparse point cloud along with the location of each camera in the
	 * scene.
	public void visualizeSparseCloud() {
		List<Point3D_F64> cloudXyz = new ArrayList<>();
		Point4D_F64 world = new Point4D_F64();

		// NOTE: By default the colors found below are not used. Look before to see why and how to turn them on.
		// Colorize the cloud by reprojecting the images. The math is straight forward but there's a lot of book
		// keeping that needs to be done due to the scene data structure. A class is provided to make this process easy
		var imageLookup = new LookUpImageFilesByIndex(imageFiles);
		var colorize = new ColorizeMultiViewStereoResults<>(new LookUpColorRgbFormats.PL_U8(), imageLookup);

		DogArray_I32 rgb = new DogArray_I32();
				( viewIdx ) -> viewIdx + "", // String encodes the image's index
				( pointIdx, r, g, b ) -> rgb.set(pointIdx, (r << 16) | (g << 8) | b)); // Assign the RGB color

		// Convert the structure into regular 3D points from homogenous
		for (int i = 0; i < scene.points.size; i++) {
			// If the point is at infinity it's not clear what to do. It would be best to skip it then the color
			// array would be out of sync. Let's just throw it far far away then.
			if (world.w == 0.0)
				cloudXyz.add(new Point3D_F64(0, 0, Double.MAX_VALUE));
				cloudXyz.add(new Point3D_F64(world.x/world.w, world.y/world.w, world.z/world.w));

		PointCloudViewer viewer = VisualizeData.createPointCloudViewer();
		// We just did a bunch of work to look up the true color of points, however for sparse data it's easy to see
		// the structure with psuedo color. Comment out the line below to see the true color.
		viewer.setColorizer(new TwoAxisRgbPlane.Z_XY(1.0).fperiod(40));
		viewer.addCloud(cloudXyz, rgb.data);

		SwingUtilities.invokeLater(() -> {
			// Show where the cameras are
			BoofSwingUtil.visualizeCameras(scene, viewer);

			// Size the window and show it to the user
			viewer.getComponent().setPreferredSize(new Dimension(600, 600));
			ShowImages.showWindow(viewer.getComponent(), "Refined Scene", true);