find nearest neighbours for 2D and 3D point clouds
find nearest neighbours for point clouds using a kd-tree search. This is just a wrapper of the function vcgKDtree from
package Rvcg. Wwraps the function vcgKDtree from package 'Rvcg' (for backward compatibility )
mcNNindex(target, query, cores = parallel::detectCores(), k = k, ...)
target |
|
query |
|
cores |
integer: amount of CPU-cores to be used. Only available on systems with OpenMP support. |
k |
integer: how many closest points are sought. |
... |
additional arguments - currently unused. |
l x k matrix containing indices of closest points.
require(rgl) data(nose) # find closest vertex on surface for each landmark clost <- mcNNindex(vert2points(shortnose.mesh),shortnose.lm, k=1, mc.cores=1) ## Not run: spheres3d(vert2points(shortnose.mesh)[clost,],col=2,radius=0.3) spheres3d(shortnose.lm,radius=0.3) wire3d(shortnose.mesh) ## End(Not run)
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