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Yes sorry, it's subsampling.

And I'm not sure about what exactly you were expecting some clarifications?

And to tell other users that the subject has been 'fully' answered, you can edit the thread title with [SOLVED] at the beginning for instance.

Do you mean this part of my question?

"In any case, I have used the height normalized dataset because I wasn't sure or it wasn't so clear how to deal with the subsampling, especially with regards to my case of different altitudes. Can you clarify this?"

The clarification I was asking about is with respect to the subsampling. If I am subsampling the low-altitude pointcloud (because we assume it should be denser), how do I arrive at the minimum space between points, or even choose the method of sub-sampling? Especially if they are from different fields.

Sadly in this case you can only reduce the number of points so that all the clouds have the same density. That's maybe a shame if you have only one cloud with a low density...

I'm not sure exactly how to deduce what the right 'distance' between points should be. You could try with the good trial an error method (or a kind of binary search). But you'll have to compute the surface density each time until it matches the density of the low density cloud... Or maybe try your luck with some basic geometry where you approximate a surface per point and compute the 'distance' as the square root of this unitary surface?

Thank you so much or all the clarifications @Daniel.

They were helpful