Example Stereo Single Camera

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Associated inlier features between two views
Associated inlier features between two views
Stereo disparity image
3D point cloud
Stereo disparity image 3D point cloud

In this scenario we wish to compute a dense point cloud from two views taken with the same calibrated camera. Because the intrinsic parameters are known, it is easier to converge towards a valid solution than the entirely uncalibrated scenario.

Because the extrinsic parameters (translation and rotation) between the two views is unknown the scene's structure can only be recovered up to a scale factor. In this example natural features are used to determine the geometric relationship between the two views. The algorithm can be summarized as follows:

  • Load camera calibration and two images
  • Detect, describe, and associate image features
  • Compute camera motion (Essential matrix)
  • Rectify image pair
  • Compute dense stereo disparity
  • Convert into 3D point cloud

Example File:


  • Point feature association
  • Epipolar geometry
  • Rectification
  • Dense stereo processing

Related Videos:

Related Tutorials/Example Code:

Example Code

 * Example demonstrating how to use to images taken from a single calibrated camera to create a stereo disparity image,
 * from which a dense 3D point cloud of the scene can be computed. For this technique to work the camera's motion
 * needs to be approximately tangential to the direction the camera is pointing. The code below assumes that the first
 * image is to the left of the second image.
 * @author Peter Abeles
public class ExampleStereoTwoViewsOneCamera {

	// Specifies the disparity values which will be considered
	private static final int disparityMin = 15;
	private static final int disparityRange = 85;

	public static void main( String[] args ) {
		// specify location of images and calibration
		String calibDir = UtilIO.pathExample("calibration/mono/Sony_DSC-HX5V_Chess/");
		String imageDir = UtilIO.pathExample("stereo/");

		// Camera parameters
		CameraPinholeBrown intrinsic = CalibrationIO.load(new File(calibDir, "intrinsic.yaml"));

		// Input images from the camera moving left to right
		BufferedImage origLeft = UtilImageIO.loadImage(imageDir, "mono_wall_01.jpg");
		BufferedImage origRight = UtilImageIO.loadImage(imageDir, "mono_wall_02.jpg");

		// Input images with lens distortion
		GrayU8 distortedLeft = ConvertBufferedImage.convertFrom(origLeft, (GrayU8)null);
		GrayU8 distortedRight = ConvertBufferedImage.convertFrom(origRight, (GrayU8)null);

		// matched features between the two images
		List<AssociatedPair> matchedFeatures = ExampleComputeFundamentalMatrix.computeMatches(origLeft, origRight);

		// convert from pixel coordinates into normalized image coordinates
		List<AssociatedPair> matchedCalibrated = convertToNormalizedCoordinates(matchedFeatures, intrinsic);

		// Robustly estimate camera motion
		List<AssociatedPair> inliers = new ArrayList<>();
		Se3_F64 leftToRight = estimateCameraMotion(intrinsic, matchedCalibrated, inliers);

		drawInliers(origLeft, origRight, intrinsic, inliers);

		// Rectify and remove lens distortion for stereo processing
		DMatrixRMaj rectifiedK = new DMatrixRMaj(3, 3);
		DMatrixRMaj rectifiedR = new DMatrixRMaj(3, 3);
		GrayU8 rectifiedLeft = distortedLeft.createSameShape();
		GrayU8 rectifiedRight = distortedRight.createSameShape();
		GrayU8 rectifiedMask = distortedLeft.createSameShape();

		rectifyImages(distortedLeft, distortedRight, leftToRight, intrinsic, intrinsic,
				rectifiedLeft, rectifiedRight, rectifiedMask, rectifiedK, rectifiedR);

		// compute disparity
		ConfigDisparityBMBest5 config = new ConfigDisparityBMBest5();
		config.errorType = DisparityError.CENSUS;
		config.disparityMin = disparityMin;
		config.disparityRange = disparityRange;
		config.subpixel = true;
		config.regionRadiusX = config.regionRadiusY = 5;
		config.maxPerPixelError = 20;
		config.validateRtoL = 1;
		config.texture = 0.1;
		StereoDisparity<GrayU8, GrayF32> disparityAlg =
				FactoryStereoDisparity.blockMatchBest5(config, GrayU8.class, GrayF32.class);

		// process and return the results
		disparityAlg.process(rectifiedLeft, rectifiedRight);
		GrayF32 disparity = disparityAlg.getDisparity();
		RectifyImageOps.applyMask(disparity, rectifiedMask, 0);

		// show results
		BufferedImage visualized = VisualizeImageData.disparity(disparity, null, disparityRange, 0);

		BufferedImage outLeft = ConvertBufferedImage.convertTo(rectifiedLeft, null);
		BufferedImage outRight = ConvertBufferedImage.convertTo(rectifiedRight, null);

		ShowImages.showWindow(new RectifiedPairPanel(true, outLeft, outRight), "Rectification", true);
		ShowImages.showWindow(visualized, "Disparity", true);

		showPointCloud(disparity, outLeft, leftToRight, rectifiedK, rectifiedR, disparityMin, disparityRange);

		System.out.println("Total found " + matchedCalibrated.size());
		System.out.println("Total Inliers " + inliers.size());

	 * Estimates the camera motion robustly using RANSAC and a set of associated points.
	 * @param intrinsic Intrinsic camera parameters
	 * @param matchedNorm set of matched point features in normalized image coordinates
	 * @param inliers OUTPUT: Set of inlier features from RANSAC
	 * @return Found camera motion. Note translation has an arbitrary scale
	public static Se3_F64 estimateCameraMotion( CameraPinholeBrown intrinsic,
												List<AssociatedPair> matchedNorm, List<AssociatedPair> inliers ) {
		ModelMatcherMultiview<Se3_F64, AssociatedPair> epipolarMotion =
				FactoryMultiViewRobust.baselineRansac(new ConfigEssential(), new ConfigRansac(200, 0.5));
		epipolarMotion.setIntrinsic(0, intrinsic);
		epipolarMotion.setIntrinsic(1, intrinsic);

		if (!epipolarMotion.process(matchedNorm))
			throw new RuntimeException("Motion estimation failed");

		// save inlier set for debugging purposes

		return epipolarMotion.getModelParameters();

	 * Convert a set of associated point features from pixel coordinates into normalized image coordinates.
	public static List<AssociatedPair> convertToNormalizedCoordinates( List<AssociatedPair> matchedFeatures, CameraPinholeBrown intrinsic ) {

		Point2Transform2_F64 p_to_n = LensDistortionFactory.narrow(intrinsic).undistort_F64(true, false);

		List<AssociatedPair> calibratedFeatures = new ArrayList<>();

		for (AssociatedPair p : matchedFeatures) {
			AssociatedPair c = new AssociatedPair();

			p_to_n.compute(p.p1.x, p.p1.y, c.p1);
			p_to_n.compute(p.p2.x, p.p2.y, c.p2);


		return calibratedFeatures;

	 * Remove lens distortion and rectify stereo images
	 * @param distortedLeft Input distorted image from left camera.
	 * @param distortedRight Input distorted image from right camera.
	 * @param leftToRight Camera motion from left to right
	 * @param intrinsicLeft Intrinsic camera parameters
	 * @param rectifiedLeft Output rectified image for left camera.
	 * @param rectifiedRight Output rectified image for right camera.
	 * @param rectifiedMask Mask that indicates invalid pixels in rectified image. 1 = valid, 0 = invalid
	 * @param rectifiedK Output camera calibration matrix for rectified camera
	public static <T extends ImageBase<T>>
	void rectifyImages( T distortedLeft,
						T distortedRight,
						Se3_F64 leftToRight,
						CameraPinholeBrown intrinsicLeft,
						CameraPinholeBrown intrinsicRight,
						T rectifiedLeft,
						T rectifiedRight,
						GrayU8 rectifiedMask,
						DMatrixRMaj rectifiedK,
						DMatrixRMaj rectifiedR ) {
		RectifyCalibrated rectifyAlg = RectifyImageOps.createCalibrated();

		// original camera calibration matrices
		DMatrixRMaj K1 = PerspectiveOps.pinholeToMatrix(intrinsicLeft, (DMatrixRMaj)null);
		DMatrixRMaj K2 = PerspectiveOps.pinholeToMatrix(intrinsicRight, (DMatrixRMaj)null);

		rectifyAlg.process(K1, new Se3_F64(), K2, leftToRight);

		// rectification matrix for each image
		DMatrixRMaj rect1 = rectifyAlg.getUndistToRectPixels1();
		DMatrixRMaj rect2 = rectifyAlg.getUndistToRectPixels2();

		// New calibration matrix,

		// Adjust the rectification to make the view area more useful
		ImageDimension rectShape = new ImageDimension();
		RectifyImageOps.fullViewLeft(intrinsicLeft, rect1, rect2, rectifiedK, rectShape);
//		RectifyImageOps.allInsideLeft(intrinsicLeft, rect1, rect2, rectifiedK, rectShape);
		// Taking in account the relative rotation between the image axis and the baseline is important in
		// this scenario since a person can easily hold the camera at an odd angle. If you don't adjust
		// the rectified image size you might end up with a lot of wasted pixels and a low resolution model!
		rectifiedLeft.reshape(rectShape.width, rectShape.height);
		rectifiedRight.reshape(rectShape.width, rectShape.height);

		// undistorted and rectify images
		FMatrixRMaj rect1_F32 = new FMatrixRMaj(3, 3);
		FMatrixRMaj rect2_F32 = new FMatrixRMaj(3, 3);
		ConvertMatrixData.convert(rect1, rect1_F32);
		ConvertMatrixData.convert(rect2, rect2_F32);

		// Extending the image prevents a harsh edge reducing false matches at the image border
		// SKIP is another option, possibly a tinny bit faster, but has a harsh edge which will need to be filtered
		ImageDistort<T, T> distortLeft =
				RectifyDistortImageOps.rectifyImage(intrinsicLeft, rect1_F32, BorderType.EXTENDED, distortedLeft.getImageType());
		ImageDistort<T, T> distortRight =
				RectifyDistortImageOps.rectifyImage(intrinsicRight, rect2_F32, BorderType.EXTENDED, distortedRight.getImageType());

		distortLeft.apply(distortedLeft, rectifiedLeft, rectifiedMask);
		distortRight.apply(distortedRight, rectifiedRight);

	 * Draw inliers for debugging purposes. Need to convert from normalized to pixel coordinates.
	public static void drawInliers( BufferedImage left, BufferedImage right, CameraPinholeBrown intrinsic,
									List<AssociatedPair> normalized ) {
		Point2Transform2_F64 n_to_p = LensDistortionFactory.narrow(intrinsic).distort_F64(false, true);

		List<AssociatedPair> pixels = new ArrayList<>();

		for (AssociatedPair n : normalized) {
			AssociatedPair p = new AssociatedPair();

			n_to_p.compute(n.p1.x, n.p1.y, p.p1);
			n_to_p.compute(n.p2.x, n.p2.y, p.p2);


		// display the results
		AssociationPanel panel = new AssociationPanel(20);
		panel.setImages(left, right);

		ShowImages.showWindow(panel, "Inlier Features", true);

	 * Show results as a point cloud
	public static void showPointCloud( ImageGray disparity, BufferedImage left,
									   Se3_F64 motion, DMatrixRMaj rectifiedK, DMatrixRMaj rectifiedR,
									   int disparityMin, int disparityRange ) {
		DisparityToColorPointCloud d2c = new DisparityToColorPointCloud();
		PointCloudWriter.CloudArraysF32 cloud = new PointCloudWriter.CloudArraysF32();

		double baseline = motion.getT().norm();
		d2c.configure(baseline, rectifiedK, rectifiedR, null, disparityMin, disparityRange);
		d2c.process(disparity, UtilDisparitySwing.wrap(left), cloud);

		CameraPinhole rectifiedPinhole = PerspectiveOps.matrixToPinhole(rectifiedK, disparity.width, disparity.height, null);

		// skew the view to make the structure easier to see
		Se3_F64 cameraToWorld = SpecialEuclideanOps_F64.eulerXyz(-baseline*5, 0, 0, 0, 0.2, 0, null);

		PointCloudViewer pcv = VisualizeData.createPointCloudViewer();
		pcv.addCloud(cloud.cloudXyz, cloud.cloudRgb);

		pcv.getComponent().setPreferredSize(new Dimension(left.getWidth(), left.getHeight()));
		ShowImages.showWindow(pcv.getComponent(), "Point Cloud", true);