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Find neighbors within a radius of a point in the point cloud

`[indices,dists] = findNeighborsInRadius(ptCloud,point,radius)`

`[indices,dists] = findNeighborsInRadius(ptCloud,point,radius,camMatrix)`

`[indices,dists] = findNeighborsInRadius(___,Name,Value)`

`[`

returns the neighbors within a radius of a query point in the input point cloud. The input
point cloud can be an unorganized or organized point cloud data. The neighbors within a
radius of the query point are computed by using the Kd-tree based search algorithm.`indices`

,`dists`

] = findNeighborsInRadius(`ptCloud`

,`point`

,`radius`

)

`[`

returns the neighbors within a radius of a query point in the input point cloud. The input
point cloud is an organized point cloud data generated by a depth camera. The neighbors
within a radius of the query point are determined using fast approximate neighbor search
algorithm. The function uses the camera projection matrix `indices`

,`dists`

] = findNeighborsInRadius(`ptCloud`

,`point`

,`radius`

,`camMatrix`

)`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.

This function 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.

`[`

specifies options using one or more name-value pair arguments in addition to the input
arguments in the preceding syntaxes.`indices`

,`dists`

] = findNeighborsInRadius(___,`Name,Value`

)

[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.