Unconventional k-d tree partitioning algorithm

Post on Patreon, HIP file available:
https://www.patreon.com/posts/unconventional-k-16752724

As opposed to my 'Parametric Clusters' project where we utilise some altered Voronoi Fractures, this is a rather unconventional approach to a classic k-dimensional tree algorithm, which is basically a space-partitioning data structure that will only allow for quadrilateral outcomes.

Traditional K-dimensional algorithms will normally use neighbouring points to define the search trees and set its constraints. Instead, we randomize the position of every perpendicular hyperplane that will split the primitive into two pieces at every iteration, for as many times as want.

There are a few interesting tips and techniques to be learned from here that can also be easily expandable, and I hope this may somehow be of help or even entertainment for you.

Thank you.