Example Multiview Reconstruction Dense

Video showing resulting point cloud. Red squares represent camera view locations.

After the sparse reconstruction has been applied and the extrinsic and intrinsic parameters of the scene are known, the next step it to compute a dense reconstruction. Internally key frames are selected to perform multi-baseline stereo on and then their resulting point clouds are all combined together into a single cloud.

Example Code:

• ExampleMultiViewDenseReconstruction.java

Concepts:

• Structure from Motion
• Sparse Bundle Adjustment
• Multi Baseline Stereo
• Uncalibrated Sparse Reconstruction

Videos:

• Improvements in v0.38

Tutorials

• Photogrammetry / 3D Reconstruction on Desktop

Example Code

/**
 * A dense point cloud is created using a previously computed sparse reconstruction and a basic implementation of
 * multiview stereo (MVS). This approach to MVS works by identifying "center" views which have the best set of
 * neighbors for stereo computations using a heuristic. Then a global point cloud is created from the "center" view
 * disparity images while taking care to avoid adding duplicate points.
 *
 * @author Peter Abeles
 */
public class ExampleMultiViewDenseReconstruction {
	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);

		// Looks up images based on their index in the file list
		var imageLookup = new LookUpImageFilesByIndex(example.imageFiles);

		// We will use a high level algorithm that does almost all the work for us. It is highly configurable
		// and just about every parameter can be tweaked using its Config. Internal algorithms can be accessed
		// and customize directly if needed. Specifics for how it work is beyond this example but the code
		// is easily accessible.

		// Let's do some custom configuration for this scenario
		var config = new ConfigSparseToDenseCloud();
		config.disparity.approach = ConfigDisparity.Approach.SGM;
		ConfigDisparitySGM configSgm = config.disparity.approachSGM;
		configSgm.validateRtoL = 0;
		configSgm.texture = 0.75;
		configSgm.disparityRange = 250;
		configSgm.paths = ConfigDisparitySGM.Paths.P4;
		configSgm.configBlockMatch.radiusX = 3;
		configSgm.configBlockMatch.radiusY = 3;

		// Create the sparse to dense reconstruction using a factory
		SparseSceneToDenseCloud<GrayU8> sparseToDense =
				FactorySceneReconstruction.sparseSceneToDenseCloud(config, ImageType.SB_U8);

		// To help make the time go by faster while we wait about 1 to 2 minutes for it to finish, let's print stuff
		sparseToDense.getMultiViewStereo().setVerbose(
				System.out, BoofMiscOps.hashSet(BoofVerbose.RECURSIVE, BoofVerbose.RUNTIME));

		// To visualize intermediate results we will add a listener. This will show fused disparity images
		sparseToDense.getMultiViewStereo().setListener(new MultiViewStereoFromKnownSceneStructure.Listener<>() {
			@Override
			public void handlePairDisparity( String left, String right, GrayU8 rect0, GrayU8 rect1,
											 GrayF32 disparity, GrayU8 mask, DisparityParameters parameters ) {
				// Uncomment to display individual stereo pairs. Commented out by default because it generates
				// a LOT of windows
//				BufferedImage outLeft = ConvertBufferedImage.convertTo(rect0, null);
//				BufferedImage outRight = ConvertBufferedImage.convertTo(rect1, null);
//
//				ShowImages.showWindow(new RectifiedPairPanel(true, outLeft, outRight), "Rectification: "+left+" "+right);
//				BufferedImage colorized = VisualizeImageData.disparity(disparity, null, parameters.disparityRange, 0);
//				ShowImages.showWindow(colorized, "Disparity " + left + " " + right);
			}

			@Override
			public void handleFusedDisparity( String name,
											  GrayF32 disparity, GrayU8 mask, DisparityParameters parameters ) {
				// You can also do custom filtering of the disparity image in this function. If the line below is
				// uncommented then points which are far away will be marked as invalid
//				PixelMath.operator1(disparity, ( v ) -> v >= 20 ? v : parameters.disparityRange, disparity);

				// Display the disparity for each center view
				BufferedImage colorized = VisualizeImageData.disparity(disparity, null, parameters.disparityRange, 0);
				ShowImages.showWindow(colorized, "Center " + name);
			}
		});

		// It needs a look up table to go from SBA view index to image name. It loads images as needed to perform
		// stereo disparity
		var viewToId = new TIntObjectHashMap<String>();
		BoofMiscOps.forIdx(example.working.listViews, ( workIdxI, wv ) -> viewToId.put(wv.index, wv.pview.id));
		if (!sparseToDense.process(example.scene, viewToId, imageLookup))
			throw new RuntimeException("Dense reconstruction failed!");

		saveCloudToDisk(sparseToDense);

		// Display the dense cloud
		visualizeInPointCloud(sparseToDense.getCloud(), sparseToDense.getColorRgb(), example.scene);
	}

	private static void saveCloudToDisk( SparseSceneToDenseCloud<GrayU8> sparseToDense ) {
		// Save the dense point cloud to disk in PLY format
		try (FileOutputStream out = new FileOutputStream("saved_cloud.ply")) {
			// Filter points which are far away to make it easier to view in 3rd party viewers that auto scale
			// You might need to adjust the threshold for your application if too many points are cut
			double distanceThreshold = 50.0;
			List<Point3D_F64> cloud = sparseToDense.getCloud();
			DogArray_I32 colorsRgb = sparseToDense.getColorRgb();

			DogArray<Point3dRgbI_F64> filtered = PointCloudUtils_F64.filter(
					( idx, p ) -> p.setTo(cloud.get(idx)), colorsRgb::get, cloud.size(),
					( idx ) -> cloud.get(idx).norm() <= distanceThreshold, null);

			PointCloudIO.save3D(PointCloudIO.Format.PLY, PointCloudReader.wrapF64RGB(filtered.toList()), true, out);
		} catch (IOException e) {
			e.printStackTrace();
		}
	}

	public static void visualizeInPointCloud( List<Point3D_F64> cloud, DogArray_I32 colorsRgb,
											  SceneStructureMetric structure ) {
		PointCloudViewer viewer = VisualizeData.createPointCloudViewer();
		viewer.setFog(true);
		viewer.setDotSize(1);
		viewer.setTranslationStep(0.15);
		viewer.addCloud(( idx, p ) -> p.setTo(cloud.get(idx)), colorsRgb::get, cloud.size());
		viewer.setCameraHFov(UtilAngle.radian(60));

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

			// Display the point cloud
			viewer.getComponent().setPreferredSize(new Dimension(600, 600));
			ShowImages.showWindow(viewer.getComponent(), "Dense Reconstruction Cloud", true);
		});
	}
}