Example Multiview Uncalibrated Reconstruction Sparse

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

	PairwiseImageGraph pairwise = null;
	LookUpSimilarImages dbSimilar;
	LookUpCameraInfo dbCams = new LookUpCameraInfo();
	SceneWorkingGraph working = null;
	SceneStructureMetric scene = null;

	boolean rebuild = false;

	public static void main( String[] args ) {
		var example = new ExampleMultiViewSparseReconstruction();
		example.compute("tree_snow_01.mp4", true);
//		example.compute("ditch_02.mp4", true);
//		example.compute("holiday_display_01.mp4", true);
//		example.compute("log_building_02.mp4", true);
//		example.compute("drone_park_01.mp4", false);
//		example.compute("stone_sign.mp4", true);


	public void compute( String videoName, boolean sequential ) {
		// 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);

		// Attempt to reload intermediate results if previously computed
		if (!rebuild) {
			try {
				pairwise = MultiViewIO.load(new File(workDirectory, "pairwise.yaml").getPath(), (PairwiseImageGraph)null);
			} catch (UncheckedIOException ignore) {}

			try {
				working = MultiViewIO.load(new File(workDirectory, "working.yaml").getPath(), pairwise, null);
			} catch (UncheckedIOException ignore) {}

			try {
				scene = MultiViewIO.load(new File(workDirectory, "structure.yaml").getPath(), (SceneStructureMetric)null);
			} catch (UncheckedIOException ignore) {}

		// 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()) {
			imageFiles = UtilIO.listSmart(String.format("glob:%s/images/*.png", workDirectory), true, ( f ) -> true);
		} else {
			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()) {
					InterleavedU8 image = sequence.next();
					File imageFile = new File(imageDirectory, String.format("frame%04d.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)
					UtilImageIO.saveImage(image, imageFile.getPath());
			}, "Video Decoding");

		// Only determine the visual relationship between images if needed
		if (pairwise == null || working == null) {
			if (sequential) {
			} else {

		if (pairwise == null)
		if (working == null)
		if (scene == null)

		var rod = new Rodrigues_F64();
		for (PairwiseImageGraph.View pv : pairwise.nodes.toList()) {
			if (!working.containsView(pv.id))
			SceneWorkingGraph.View wv = working.lookupView(pv.id);
			int order = working.listViews.indexOf(wv);
			ConvertRotation3D_F64.matrixToRodrigues(wv.world_to_view.R, rod);
			BundlePinholeSimplified intrinsics = working.getViewCamera(wv).intrinsic;
			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, intrinsics.f, intrinsics.k1, intrinsics.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 similarImagesFromSequence() {
		System.out.println("### Creating Similar Images from an ordered set of images");

		// Configure the KLT tracker
		ConfigPointTracker configTracker = FactorySceneRecognition.createDefaultTrackerConfig();

		PointTracker<GrayU8> tracker = FactoryPointTracker.tracker(configTracker, GrayU8.class, null);
		var activeTracks = new ArrayList<PointTrack>();

		var config = new ConfigSimilarImagesTrackThenMatch();

		final var dbSimilar = FactorySceneReconstruction.createTrackThenMatch(config, ImageType.SB_U8);
		dbSimilar.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));

		// 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);
				Objects.requireNonNull(frame, "Failed to load image");
				if (first) {
					first = false;
					dbSimilar.initialize(frame.width, frame.height);
					dbCams.addCameraCanonical(frame.width, frame.height, 60.0);

				int activeCount = tracker.getTotalActive();
				int droppedCount = tracker.getDroppedTracks(null).size();
				dbSimilar.processFrame(frame, activeTracks, tracker.getFrameID());
				String id = frameId + "";
				System.out.println("frame id = " + id + " active=" + activeCount + " dropped=" + droppedCount);

				// Everything maps to the same camera
				dbCams.addView(id, 0);

		}, "Finding Similar");

		this.dbSimilar = dbSimilar;

	 * Assumes that the images are complete unsorted
	private void similarImagesFromUnsorted() {
		System.out.println("### Creating Similar Images from unordered images");

		var config = new ConfigSimilarImagesSceneRecognition();

		final var similarImages = FactorySceneReconstruction.createSimilarImages(config, ImageType.SB_U8);
		similarImages.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));

		// Track features across the entire sequence and save the results
		BoofMiscOps.profile(() -> {
			for (int frameId = 0; frameId < imageFiles.size(); frameId++) {
				String filePath = imageFiles.get(frameId);
				GrayU8 frame = UtilImageIO.loadImage(filePath, GrayU8.class);
				Objects.requireNonNull(frame, "Failed to load image");

				String viewID = frameId + "";

				similarImages.addImage(viewID, frame);
				// Everything maps to the same camera
				if (frameId == 0)
					dbCams.addCameraCanonical(frame.width, frame.height, 60.0);
				dbCams.addView(viewID, 0);

		}, "Finding Similar");

		this.dbSimilar = similarImages;

	 * 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() {
		System.out.println("### Creating Pairwise");
		var config = new ConfigGeneratePairwiseImageGraph();
		GeneratePairwiseImageGraph generatePairwise = FactorySceneReconstruction.generatePairwise(config);
		BoofMiscOps.profile(() -> {
			generatePairwise.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));
			generatePairwise.process(dbSimilar, dbCams);
		}, "Created Pairwise graph");
		pairwise = generatePairwise.getGraph();

		var savePath = new File(workDirectory, "pairwise.yaml");
		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() {
		System.out.println("### Metric Reconstruction");

		var metric = new MetricFromUncalibratedPairwiseGraph();
		metric.setVerbose(System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE));
		BoofMiscOps.profile(() -> {
			if (!metric.process(dbSimilar, dbCams, pairwise)) {
				System.err.println("Reconstruction failed");
		}, "Metric Reconstruction");

		working = metric.getLargestScene();

		var savePath = new File(workDirectory, "working.yaml");
		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() {
		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.metricSba.keepFraction = 0.95;
			refine.metricSba.getSba().setVerbose(System.out, null);
			if (!refine.process(dbSimilar, working)) {
				System.out.println("SBA REFINE FAILED");
		}, "Bundle Adjustment refine");
		scene = refine.metricSba.structure;

		var savePath = new File(workDirectory, "structure.yaml");
		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(( idx, p ) -> p.setTo(cloudXyz.get(idx)), rgb::get, rgb.size);

		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);

			var copy = new DogArray<>(Point3dRgbI_F64::new);

			try (var out = new FileOutputStream("saved_cloud.ply")) {
				PointCloudIO.save3D(PointCloudIO.Format.PLY, PointCloudReader.wrapF64RGB(copy.toList()), true, out);
			} catch (IOException e) {