Package com.irurueta.geometry.estimators

Class PROMedSPoint3DRobustEstimator

java.lang.Object
com.irurueta.geometry.estimators.Point3DRobustEstimator
com.irurueta.geometry.estimators.PROMedSPoint3DRobustEstimator
public class PROMedSPoint3DRobustEstimator extends Point3DRobustEstimator
Finds the best 3D point for provided collection of 3D points using PROMedS algorithm.

  • Field Details

    • DEFAULT_STOP_THRESHOLD

      public static final double DEFAULT_STOP_THRESHOLD
      Default value to be used for stop threshold. Stop threshold can be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.
      See Also:

    • MIN_STOP_THRESHOLD

      public static final double MIN_STOP_THRESHOLD
      Minimum allowed stop threshold value.
      See Also:

    • stopThreshold

      private double stopThreshold
      Threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.

    • qualityScores

      private double[] qualityScores
      Quality scores corresponding to each provided point. The larger the score value the better the quality of the sample.

  • Constructor Details

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator()
      Constructor.

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(List<Plane> planes)
      Constructor with planes.
      Parameters:
      planes - 3D planes to estimate a 3D point.
      Throws:
      IllegalArgumentException - if provided list of planes doesn't have a size greater or equal than MINIMUM_SIZE.

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(Point3DRobustEstimatorListener listener)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(Point3DRobustEstimatorListener listener, List<Plane> planes)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      planes - 3D lines to estimate a 3D point.
      Throws:
      IllegalArgumentException - if provided list of planes doesn't have a size greater or equal than MINIMUM_SIZE.

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(double[] qualityScores)
      Constructor.
      Parameters:
      qualityScores - quality scores corresponding to each provided plane.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 3 planes).

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(List<Plane> planes, double[] qualityScores)
      Constructor with planes.
      Parameters:
      planes - 3D planes to estimate a 3D point.
      qualityScores - quality scores corresponding to each provided plane.
      Throws:
      IllegalArgumentException - if provided list of planes doesn't have the same size as the list of provided quality scores, or it their size is not greater or equal than MINIMUM_SIZE.

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(Point3DRobustEstimatorListener listener, double[] qualityScores)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      qualityScores - quality scores corresponding to each provided plane.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 3 planes).

    • PROMedSPoint3DRobustEstimator

      public PROMedSPoint3DRobustEstimator(Point3DRobustEstimatorListener listener, List<Plane> planes, double[] qualityScores)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      planes - 3D planes to estimate a 3D point.
      qualityScores - quality scores corresponding to each provided plane.
      Throws:
      IllegalArgumentException - if provided list of planes doesn't have the same size as the list of provided quality scores, or it their size is not greater or equal than MINIMUM_SIZE.

  • Method Details

    • getStopThreshold

      public double getStopThreshold()
      Returns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.
      Returns:
      stop threshold to stop the algorithm prematurely when a certain accuracy has been reached.

    • setStopThreshold

      public void setStopThreshold(double stopThreshold) throws LockedException
      Sets threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.
      Parameters:
      stopThreshold - stop threshold to stop the algorithm prematurely when a certain accuracy has been reached.
      Throws:
      IllegalArgumentException - if provided value is zero or negative.
      LockedException - if robust estimator is locked because an estimation is already in progress.

    • getQualityScores

      public double[] getQualityScores()
      Returns quality scores corresponding to each provided point. The larger the score value the better the quality of the sampled point.
      Overrides:
      getQualityScores in class Point3DRobustEstimator
      Returns:
      quality scores corresponding to each point.

    • setQualityScores

      public void setQualityScores(double[] qualityScores) throws LockedException
      Sets quality scores corresponding to each provided point. The larger the score value the better the quality of the sampled point.
      Overrides:
      setQualityScores in class Point3DRobustEstimator
      Parameters:
      qualityScores - quality scores corresponding to each point.
      Throws:
      LockedException - if robust estimator is locked because an estimation is already in progress.
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 3 samples).

    • isReady

      public boolean isReady()
      Indicates if estimator is ready to start the 3D point estimation. This is true when input data (i.e. 2D points and quality scores) are provided and a minimum of MINIMUM_SIZE points are available.
      Overrides:
      isReady in class Point3DRobustEstimator
      Returns:
      true if estimator is ready, false otherwise.

    • estimate

      public Point3D estimate() throws LockedException, NotReadyException, com.irurueta.numerical.robust.RobustEstimatorException
      Estimates a 3D point using a robust estimator and the best set of 3D planes that intersect into the estimated 3D point.
      Specified by:
      estimate in class Point3DRobustEstimator
      Returns:
      a 3D point.
      Throws:
      LockedException - if robust estimator is locked because an estimation is already in progress.
      NotReadyException - if provided input data is not enough to start the estimation.
      com.irurueta.numerical.robust.RobustEstimatorException - if estimation fails for any reason (i.e. numerical instability, no solution available, etc).

    • getMethod

      public com.irurueta.numerical.robust.RobustEstimatorMethod getMethod()
      Returns method being used for robust estimation.
      Specified by:
      getMethod in class Point3DRobustEstimator
      Returns:
      method being used for robust estimation.

    • getRefinementStandardDeviation

      protected double getRefinementStandardDeviation()
      Gets standard deviation used for Levenberg-Marquardt fitting during refinement. Returned value gives an indication of how much variance each residual has. Typically, this value is related to the threshold used on each robust estimation, since residuals of found inliers are within the range of such threshold.
      Specified by:
      getRefinementStandardDeviation in class Point3DRobustEstimator
      Returns:
      standard deviation used for refinement.

    • internalSetQualityScores

      private void internalSetQualityScores(double[] qualityScores)
      Sets quality scores corresponding to each provided point. This method is used internally and does not check whether instance is locked or not.
      Parameters:
      qualityScores - quality scores to be set.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE.