I need to find the diameter of the points cloud (two points with maximum distance between them) in 3-dimensional space. As a temporary solution, right now I'm just iterating through all possible pairs and comparing the distance between them, which is a very slow, O(n^2) solution.
I believe it can be done in O(n log n). It's a fairly easy task in 2D (just find the convex hull and then apply the rotating calipers algorithm), but in 3D I can't imagine how to use rotating calipers, since there is no way to order the points.
Is there any simple way to do it (or ready-to-use implementation in python or C/C++)?
PS: There are similar questions on StackOverflow, but the answers that I found only refers to Rotating Calipers (or similar) algorithms, which works fine in 2D situation but not really clear how to implement in 3D (or higher dimensionals).
While O(n log n) expected time algorithms exist in 3d, they seem tricky to implement (while staying competitive to brute-force O(n^2) algorithms).
An algorithm is described in Har-Peled 2001. The authors provide a source code than can optionally be used for optimal computation. I was not able to download the latest version, the "old" version could be enough for your purpose, or you might want to contact the authors for the code.
An alternative approach is presented in Malandain & Boissonnat 2002 and the authors provide code. Altough this algorithm is presented as approximate in higher dimensions, it could fit your purpose. Note that their code provide an implementation of Har-Peled's method for exact computation that you might also check.
In any case, in a real-world usage you should always check that your algorithm remains competitive with respect to the naïve O(n^2) approach.