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estgeotform3d

Estimate 3-D geometric transformation from matching point pairs

Since R2022b

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    Syntax

    tform = estgeotform3d(matchedPoints1,matchedPoints2,transformType)
    [tform,inlierIndex] = estgeotform3d(___)
    [tform,inlierIndex,status] = estgeotform3d(___)
    [___] = estgeotform3d(___,Name,Value)

    Description

    tform = estgeotform3d(matchedPoints1,matchedPoints2,transformType) estimates a 3-D geometric transformation between two sets of 3-D points by mapping the inliers in the matched points from one set of 3-D points matchedPoints1 to the inliers in the matched points from the other set of 3-D points matchedPoints2.

    example

    [tform,inlierIndex] = estgeotform3d(___) additionally returns a vector specifying each matched point pair as either an inlier or an outlier using the input arguments from the previous syntax.

    [tform,inlierIndex,status] = estgeotform3d(___) additionally returns a status code indicating whether or not the function could estimate a transformation and, if not, why it failed. If you do not specify the status output, the function instead returns an error for conditions that cannot produce results.

    [___] = estgeotform3d(___,Name,Value) specifies additional options using one or more name-value arguments in addition to any combination of arguments from previous syntaxes. For example, Confidence=99 sets the confidence value for finding the maximum number of inliers to 99.

    Examples

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    Open Live Script

    Load a point cloud file into the workspace.

    ptCloud1 = pcread("teapot.ply")
    ptCloud1 = 
  pointCloud with properties:

     Location: [41472x3 single]
        Count: 41472
      XLimits: [-3 3.4340]
      YLimits: [-2 2]
      ZLimits: [0 3.1500]
        Color: []
       Normal: []
    Intensity: []


    ptCloud1 = pcdownsample(ptCloud1,"random",0.25);

    Create a rigid 3-D transformation object with a 30-degree rotation about the z-axis.

    eulerAngles = [0 0 30];
trans = [0 0 0];
tform = rigidtform3d(eulerAngles,trans);

    Transform the point cloud using the transformation object.

    ptCloud2 = pctransform(ptCloud1,tform);

    To introduce noise, add random points to both point clouds.

    noise1 = rescale(rand(1000,3),-2,2);
ptCloud1 = pointCloud([ptCloud1.Location;noise1]);
noise2 = rescale(rand(1000,3),-2,2);
ptCloud2 = pointCloud([ptCloud2.Location;noise2]);

    Visualize the noisy point clouds.

    figure
pcshowpair(ptCloud1,ptCloud2)
title("Point Clouds With Added Noise")

    Figure contains an axes object. The axes object with title Point Clouds With Added Noise contains 2 objects of type scatter.

    Extract matched points from the point clouds.

    matchedPoints1 = ptCloud1.Location;
matchedPoints2 = ptCloud2.Location;

    Estimate the rigid transformation between the point clouds.

    [tformEst,inlierIndex] = estgeotform3d(matchedPoints1, ...
    matchedPoints2,"rigid");

    Extract the inlier points.

    inliersPtCloud1 = transformPointsForward(tformEst, ...
    matchedPoints1(inlierIndex,:));
inliersPtCloud2 = matchedPoints2(inlierIndex,:);

    Visualize the inliers of the aligned point clouds.

    figure
firstPtCloud = pointCloud(inliersPtCloud1);
secondPtCloud = pointCloud(inliersPtCloud2);
pcshowpair(firstPtCloud,secondPtCloud)
title("Aligned Point Clouds")

    Figure contains an axes object. The axes object with title Aligned Point Clouds contains 2 objects of type scatter.

    Input Arguments

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    Matched points from first image, specified as an M-by-3 matrix in which each row is a set of (x,y,z) coordinates and M is the number of matched points.

    Matched points from second image, specified as an M-by-3 matrix in which each row is a set of (x,y,z) coordinates and M is the number of matched points.

    Transformation type, specified as "rigid", "similarity", or "translation". Each transform type requires a minimum number of matched pairs of points to estimate a transformation. You can generally improve the accuracy of a transformation by using a larger number of matched pairs of points. This table shows the type of object associated with each transformation type and the minimum number of matched pairs of points the transformation requires.

    transformTypetform ObjectMinimum Number of Matched Pairs of Points
    "rigid"rigidtform3d3
    "similarity"simtform3d3
    "translation"transltform3d1

    Data Types: string

    Name-Value Arguments

    Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

    Example: Confidence=99 sets the confidence value for finding the maximum number of inliers to 99.

    Maximum number of random trials, specified as a positive integer. This value specifies the number of randomized attempts the function makes to find matching point pairs. Specifying a higher value causes the function to perform additional computations, which increases the likelihood of finding inliers.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Confidence of finding the maximum number of inliers, specified as a positive numeric scalar in the range (0, 100). Increasing this value causes the function to perform additional computations, which increases the likelihood of finding a greater number of inliers.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Maximum distance from a point to the projection of the corresponding point, specified as a positive numeric scalar. MaxDistance specifies the maximum distance, in pixels, that a point can differ from the projected location of its corresponding point to be considered an inlier. The corresponding projection is based on the estimated transform.

    The function checks for a transformation from matchedPoints1 to matchedPoints2, and then calculates the distance between the matched points in each pair after applying the transformation. If the distance between the matched points in a pair is greater than the MaxDistance value, then the pair is considered an outlier for that transformation. If the distance is less than MaxDistance, then the pair is considered an inlier.

    A matched point show in image 1 and image 2. Image one shows the point from image 2 projected back onto image 1.

    Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

    Output Arguments

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    Geometric transformation, returned as a rigidtform3d, simtform3d, or transltform3d object.

    The returned geometric transformation matrix maps the inliers in matchedPoints1 to the inliers in matchedPoints2. The function returns an object specific to the transformation type specified by the transformType input argument.

    transformTypetform
    "rigid"rigidtform3d
    "similarity"simtform3d
    "translation"transltform3d

    Inliers, returned as an M-by-1 logical vector of M point pairs. Each element contains either a logical true, 1, to indicate the point pair is an inlier, or a logical false, 0, to indicate the point pair is an outlier.

    Status code, returned as 0, 1, or 2. The status code indicates whether or not the function could estimate the transformation and, if not, why it failed.

    ValueDescription
    0No error
    1matchedPoints1 and matchedPoints2 inputs do not contain enough points
    2Not enough inliers found

    If you do not specify the status code output, the function returns an error if it cannot produce results.

    Data Types: int32

    Algorithms

    The function excludes outliers using the M-estimator sample consensus (MSAC) algorithm. The MSAC algorithm is a variant of the random sample consensus (RANSAC) algorithm. Results may not be identical between runs due to the randomized nature of the MSAC algorithm.

    References

    [1] Hartley, Richard, and Andrew Zisserman. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK ; New York: Cambridge University Press, 2003.

    [2] Torr, P.H.S., and A. Zisserman. "MLESAC: A New Robust Estimator with Application to Estimating Image Geometry." Computer Vision and Image Understanding. 78, no. 1 (April 2000): 138–56. https://doi.org/10.1006/cviu.1999.0832.

    Extended Capabilities

    C/C++ Code Generation
    Generate C and C++ code using MATLAB® Coder™.

    Version History

    Introduced in R2022b

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    See Also

    Functions

    Objects

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