Fit sphere to 3-D point cloud
fits a sphere to a point cloud that has a maximum allowable distance from an
inlier point to the sphere. The function returns a geometrical model that
describes the sphere.
model = pcfitsphere(
This function uses the M-estimator SAmple Consensus (MSAC) algorithm to find the sphere. The MSAC algorithm is a variant of the RANdom SAmple Consensus (RANSAC) algorithm.
[___] = pcfitsphere(___,
specifies options using one or more name-value arguments in addition to any
combination of arguments from previous syntaxes. For example,
MaxNumTrials=1000 sets the maximum number of random
trials to 1000.
Detect Sphere in Point Cloud
Load point cloud.
Display point cloud.
figure pcshow(ptCloud) xlabel("X(m)") ylabel("Y(m)") zlabel("Z(m)") title("Detect a sphere in a point cloud")
Set the maximum point-to-sphere distance (1cm), for sphere fitting.
maxDistance = 0.01;
Set the region of interest to constrain the search.
roi = [-inf,0.5;0.2,0.4;0.1,inf]; sampleIndices = findPointsInROI(ptCloud,roi);
Detect the globe in the point cloud and extract it.
model = pcfitsphere(ptCloud,maxDistance,SampleIndices=sampleIndices);
Plot the sphere.
hold on plot(model)
maxDistance — Maximum distance from an inlier point to the sphere
Maximum distance from an inlier point to the sphere, specified as a scalar value. Specify the distance in units that are consistent with the units you are using for the point cloud.
Specify optional pairs of arguments as
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.
Before R2021a, use commas to separate each name and value, and enclose
Name in quotes.
MaxNumTrials=1000 sets the maximum number of random trials to
SampleIndices — Linear indices of points to be sampled
 (default) | column vector
Linear indices of points to sample in the input point cloud, specified as a column vector. An
empty vector means that all points are candidates to sample in the
RANSAC iteration to fit the sphere. When you specify a subset, only
points in the subset are sampled to fit a model. Providing a subset of
points can significantly speed up the process and reduce the number of
trials. You can generate the indices vector using the
findPointsInROI method of
MaxNumTrials — Maximum number of random trials
1000 (default) | positive integer
Maximum number of random trials for finding inliers, specified as a positive integer. Increasing this value makes the output more robust but adds additional computations.
Confidence — Confidence percentage for finding maximum number of inliers
99 (default) | numeric scalar in the range [0,100]
Confidence percentage for finding maximum number of inliers, specified as a numeric scalar representing percentage, in the range [0,100]. Increasing this value makes the output more robust but adds additional computations.
model — Geometric model of sphere
Geometric model of sphere, returned as a
When the input point cloud does not contain enough valid points, or when the function cannot find enough inlier points, the coefficients for the output model are set to zero.
inlierIndices — Linear indices of inlier points
Linear indices of inlier points within the input point cloud, returned as a column vector.
outlierIndices — Linear indices of outlier points
Linear indices of outlier points within the input point cloud, returned as a column vector.
meanError — Mean square error
Mean error of the distance of inlier points to the model, returned as a scalar value.
 Torr, P. H. S. and A. Zisserman. “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry.” Computer Vision and Image Understanding. 2000.