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I am currently working on point cloud data analysis, trying to label objects which are not ground, vegetation, etc. So far I tried many clustering algorithms, with moderate success.

In my best model I did the following: Clustered data in color space (r,g,b), clustered data in (x,y,z) coordinates, and then combined these two models where points which belonged into the same two categories were merged into one super-category. K-means and mean-shift did both equally good, with K-means being much faster.

My question is: Am I on the right track, or are there much better solutions to this? Are there some algorithms (ideally as part of some major python machine learning library, but I am also open to use c++ or implement it myself) which work well to do something like grouping points together by color and position, ideally not in separate way (like I did)?

P.S.: Keep in mind I work with very large datasets (~100M points), so good performance is essential to me.

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    $\begingroup$ unsupervised? or do you have labeled points $\endgroup$ – shimao Nov 27 '18 at 18:25
  • $\begingroup$ Unfortunatelly I do not have labeled points, so unsupervised, as tag suggests. $\endgroup$ – Adam Nov 28 '18 at 9:06

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