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?
@ 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.
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