Jakob, thank you for your answer in such short time.
I found that in my case of study kD-tree may be the best choice.
I'm actually trying to check how the algorithm is working on my clouds: is there a way to visulize the points that the algorithm choose as corresponding points on the two clouds?
thank you
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13 Jun 2014
Iterative Closest Point
An implementation of various ICP (iterative closest point) features.
@ NS: directly this is not possible. Currently, the orthogonal Procrustes problem is solved, and hence a 6DOF transformation found in every iteration. If you have an analytical solution for the constrained case, you could plug that in instead, or use LM-optimization, which allows you to formulate the error function quite freely.
@ Francesco: it depends a bit on your definition of speed. kD-tree is good, and yields the same deterministic output as brute force search. Point to plane is a bit more costly per-iteration, but can yield faster convergence, and hence faster speed. It really depends, I recommend you do timing with your data.
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13 Jun 2014
Finite Iterative Closest Point
Iterative Closest Point using finite difference optimization to register 3D point clouds affine.
Hi all, i'd like to know if anybody has tried to use this algorithm on very large datasets (like point clouds with 300000-500000 points) and which options are recommended to minimize calculation times.
thank you all
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