How to compute the density of a 3D point cloud?
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Ali
el 12 de Jul. de 2020
Comentada: Ali
el 12 de Jul. de 2020
I'm trying to write a program that operates on 3D point clouds (.ply, .pcd), I need to know the density of the given point cloud file and compare it with a threshold or certain percentage to decide for some operations. How can I do that?
Two methods can be used to compute the density:
- either 'Precise': the density is estimated by counting for each point the number of neighbors N (inside a sphere of radius R)
- or 'Approximate': the density is simply estimated by determining the distance to the nearest neighbor (which is generally much faster). This distance is considered as being equivalent to the above spherical neighborhood radius R (and N = 1).
However I'm not sure how to put this into matlab code, I guess I should use pcfitplane for sphere fitting and findNearestNeighbors. Would appreciate a step by step example.
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Thiago Henrique Gomes Lobato
el 12 de Jul. de 2020
Editada: Thiago Henrique Gomes Lobato
el 12 de Jul. de 2020
You can just directly apply the definition you gave considering that the density is N/Volume. The easiest is the second one, which would be:
AllPoints = % your points
K = 1;
for idx=1:length(AllPoints)
[~,r] = findNearestNeighbors(AllPoints,AllPoints(idx,:),K);
density(idx) = 1/(4*pi*r.^3/3);
end
The first one is a little more complicated but at the same time not so much:
AllPoints = % your points
R = 1; % depends on your data
for idx=1:length(AllPoints)
Distances = sqrt( sum( (AllPoints-AllPoints(idx,:)).^2 ,2) );
Ninside = length( find(Distances<=R) );
density(idx) = Ninside/(4*pi*R.^3/3);
end
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Thiago Henrique Gomes Lobato
el 12 de Jul. de 2020
4*pi*R.^3/3 is the same as 4/3*pi*R.^3 since exponent has priority over division in matlab. For the notes the first point is important only when there's a limit to define neighborhood, and the second is already taken into account for all methods.
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