findNeighborsInRadius

Find neighbors within radius for query points in point cloud

collapse all in page

Syntax

[indices,dists,numNeighbors] = findNeighborsInRadius(ptCloud,points,radius)
[indices,dists,numNeighbors] = findNeighborsInRadius(ptCloud,points,radius,camMatrix)
[indices,dists,numNeighbors] = findNeighborsInRadius(___,Name,Value)

Description

[indices,dists,numNeighbors] = findNeighborsInRadius(ptCloud,points,radius) returns the indices of neighbors within a radius of one or more query points in the input point cloud. ptCloud can be an unorganized or organized point cloud. The neighbors within a radius of the query points are computed by using the Kd-tree based search algorithm.

example

[indices,dists,numNeighbors] = findNeighborsInRadius(ptCloud,points,radius,camMatrix) returns the neighbors within a radius of one or more query points in the input point cloud. The input point cloud is an organized point cloud generated by a depth camera. The neighbors within a radius of the query points are determined using fast approximate neighbor search algorithm.

The function uses the camera projection matrix camMatrix to know the relationship between adjacent points and hence, speeds up the search. However, the results have lower accuracy as compared to the Kd-tree based approach.

Note

  • This syntax only supports organized point cloud data produced by RGB-D sensors.

  • You can use estimateCameraMatrix to estimate camera projection matrix for the given point cloud data.

example

[indices,dists,numNeighbors] = findNeighborsInRadius(___,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments in the preceding syntaxes.

Examples

collapse all

Open Live Script

Load a set of 3-D coordinate points into the workspace.

load("xyzPoints.mat")

Create a point cloud object.

ptCloud = pointCloud(xyzPoints);

Specify two query points and a search radius.

points = [0 0 0; 0 0 3];
radius = 0.5;

For each query point, find the neighbors within the specified radius.

[indices,dists,numNeighbors] = findNeighborsInRadius(ptCloud,points,radius);

Convert the cell array of indices to an ordinary array. Then, select radial neighbors using these indices from the input point cloud.

indices = cell2mat(indices);
ptCloudB = select(ptCloud,indices);

Display the point cloud and overlay the query points and radial neighbors.

figure
pcshow(ptCloud)
hold on
plot3(points(:,1),points(:,2),points(:,3),"*")
pcshow(ptCloudB.Location,"m")
legend("Point Cloud", ...
    "Query Point", ...
    "Radial Neighbors", ...
    Location="southoutside", ...
    Color=[1 1 1])
hold off

Open Live Script

Find radial neighbors of a query point in the organized point cloud data by using the camera projection matrix. Compute the camera projection matrix from sampled point cloud data points and their corresponding image point coordinates.

Load an organized point cloud data into the workspace. The point cloud is generated by using the Kinect depth sensor. 

ld = load('object3d.mat');
ptCloud = ld.ptCloud;

Specify the step size for sampling the point cloud data.

stepSize = 100;

Sample the input point cloud and store the sampled 3-D point coordinates as a point cloud object.

indices = 1:stepSize:ptCloud.Count;
tempPtCloud = select(ptCloud,indices);

Remove invalid points from the sampled point cloud.

[tempPtCloud,validIndices] = removeInvalidPoints(tempPtCloud);

Define the 3-D world point coordinates of input point cloud.

worldPoints = tempPtCloud.Location;

Find the 2-D image coordinates corresponding to the 3-D point coordinates of input point cloud.

[Y,X] = ind2sub([size(ptCloud.Location,1),size(ptCloud.Location,2)],indices);
imagePoints = [X(validIndices)' Y(validIndices)'];

Estimate camera projection matrix from the image and the world point coordinates.

camMatrix = estimateCameraMatrix(imagePoints,worldPoints);

Specify a query point and the radius within which the neighbors are to be identified.

point = [0.4 0.3 0.2];
radius = 0.05;

Get the indices and the distances of radial neighbors. Use the point cloud method select to get the point cloud data of neighboring points.

[indices,dists] = findNeighborsInRadius(ptCloud,point,radius,camMatrix);
ptCloudB = select(ptCloud,indices);

Display the point cloud and the radial neighbors found around a query point.

figure
pcshow(ptCloud);
hold on;
pcshow(ptCloudB.Location, 'b');
legend('Point Cloud','Radial Neighbors','Location','southoutside','Color',[1 1 1] )
hold off;

Figure contains an axes object. The axes object contains 2 objects of type scatter. These objects represent Point Cloud, Radial Neighbors.

Input Arguments

collapse all

Point cloud, specified as a pointCloud object.

Query points, specified as a 1-by-3 vector or N-by-3 matrix.

  • To find the neighbors within a radius for a single query point, specify its coordinates as a 1-by-3 vector of the form [x y z].

  • To find the neighbors within a radius for multiple query points, specify them as an N-by-3 matrix. N is the number of query points. Each row of the matrix is of the form [x y z], representing the coordinates of a query point. (since R2025a)

Search radius, specified as a scalar. The function finds the neighbors within the specified radius around a query point in the input point cloud.

Camera projection matrix, specified as a 4-by-3 matrix that maps 3-D world points to 2-D image points. You can find camMatrix by using the estimateCameraMatrix function.

Name-Value Arguments

collapse all

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.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: findNeighborsInRadius(ptCloud,point,radius,'Sort',true)

Sort indices, specified as a comma-separated pair of 'Sort' and a logical scalar. When you set Sort to true, the returned indices are sorted in the ascending order based on the distance from a query point. To turn off sorting, set Sort to false.

Number of leaf nodes, specified as a comma-separated pair consisting of 'MaxLeafChecks' and an integer. When you set this value to Inf, the entire tree is searched. When the entire tree is searched, it produces exact search results. Increasing the number of leaf nodes to check increases accuracy, but reduces efficiency.

Output Arguments

collapse all

Indices of stored points, returned as an M-by-1 vector or N-by-1 cell array.

  • For a single query point, the function returns linear indices of M radial neighbors as an M-by-1 vector.

  • For multiple query points, the function returns an N-by-1 cell array. N is the number of query points. Each cell contains an M-by-1 column vector of linear indices for the radial neighbors of a query point. M is the number of radial neighbors for that query point. (since R2025a)

Distances between query points and their radial neighbors, returned as an M-by-1 vector or N-by-1 cell array.

  • For a single query point, the function returns an M-by-1 vector. The vector contains the Euclidean distances between the query point and its M radial neighbors.

  • For multiple query points, the function returns an N-by-1 cell array. N is the number of query points. Each cell contains an M-by-1 column vector of the Euclidean distances between the query point and its M radial neighbors. (since R2025a)

Since R2025a

Number of identified radial neighbors, returned as a nonnegative integer for a single query point or an N-by-1 vector of nonnegative integers for N query points.

References

[1] Muja, M. and David G. Lowe. "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration". In VISAPP International Conference on Computer Vision Theory and Applications. 2009. pp. 331–340.

Extended Capabilities

expand all

Version History

Introduced in R2015a

expand all

See Also

| | |