Examples with CodecBundleAdjustmentInTheLarge boofcv.io.geo.CodecBundleAdjustmentInTheLarge used on opensource projects

Example 1 with CodecBundleAdjustmentInTheLarge

use of boofcv.io.geo.CodecBundleAdjustmentInTheLarge in project BoofCV by lessthanoptimal.

the class ExampleBundleAdjustment method main.

public static void main(String[] args) throws IOException {
    // Because the Bundle Adjustment in the Large data set is popular, a file reader and writer is included
    // with BoofCV. BoofCV uses two data types to describe the parameters in a bundle adjustment problem
    // BundleAdjustmentSceneStructure is used for camera parameters, camera locations, and 3D points
    // BundleAdjustmentObservations for image observations of 3D points
    // ExampleMultiViewSceneReconstruction gives a better feel for these data structures or you can look
    // at the source code of CodecBundleAdjustmentInTheLarge
    var parser = new CodecBundleAdjustmentInTheLarge();
    parser.parse(new File(UtilIO.pathExample("sfm/problem-16-22106-pre.txt")));
    // Print information which gives you an idea of the problem's scale
    System.out.println("Optimizing " + parser.scene.getParameterCount() + " parameters with " + parser.observations.getObservationCount() + " observations\n\n");
    // Configure the sparse Levenberg-Marquardt solver
    var configLM = new ConfigLevenbergMarquardt();
    // Important tuning parameter. Won't converge to a good solution if picked improperly. Small changes
    // to this problem and speed up or slow down convergence and change the final result. This is true for
    // basically all solvers.
    configLM.dampeningInitial = 1e-3;
    // Improves Jacobian matrix's condition. Recommended in general but not important in this problem
    configLM.hessianScaling = true;
    var configSBA = new ConfigBundleAdjustment();
    configSBA.configOptimizer = configLM;
    // Create and configure the bundle adjustment solver
    BundleAdjustment<SceneStructureMetric> bundleAdjustment = FactoryMultiView.bundleSparseMetric(configSBA);
    // prints out useful debugging information that lets you know how well it's converging
    bundleAdjustment.setVerbose(System.out, null);
    // Specifies convergence criteria
    bundleAdjustment.configure(1e-6, 1e-6, 50);
    // Scaling each variable type so that it takes on a similar numerical value. This aids in optimization
    // Not important for this problem but is for others
    var bundleScale = new ScaleSceneStructure();
    bundleScale.applyScale(parser.scene, parser.observations);
    bundleAdjustment.setParameters(parser.scene, parser.observations);
    // Runs the solver. This will take a few minutes. 7 iterations takes about 3 minutes on my computer
    long startTime = System.currentTimeMillis();
    double errorBefore = bundleAdjustment.getFitScore();
    if (!bundleAdjustment.optimize(parser.scene)) {
        throw new RuntimeException("Bundle adjustment failed?!?");
    }
    // Print out how much it improved the model
    System.out.println();
    System.out.printf("Error reduced by %.1f%%\n", (100.0 * (errorBefore / bundleAdjustment.getFitScore() - 1.0)));
    System.out.println(BoofMiscOps.milliToHuman(System.currentTimeMillis() - startTime));
    // Return parameters to their original scaling. Can probably skip this step.
    bundleScale.undoScale(parser.scene, parser.observations);
    // Visualize the results using a point cloud viewer
    visualizeInPointCloud(parser.scene);
}