My source splines are 2D airfoil shapes, which I began by converting into b-splines (using particle swarms) from source foils (the entire UIUC database, plus every other non-patented non-copyright foil I oculd find), then I added all 10,000 NACA foils too, then I run a genetic optimizer over them all for 768 different use cases each, then I picked the top-ten best performing* ones from all that, and run 768 particle-swarm optimisations on each one again, to finally arrive at 1440 "base airfoils" (768 wings, 768 struts).
* (my solver computes lift and drag etc, and I optimsed for best CL/CD normal-distribution-weighted over a +/- 6degree AoA range)
So I am basically only using 2D, so no mesh/triangles/etc. I did write a converter to go from my code into Roland RML for milling these things, but that got way out of hand fast...
For wings and such - 2D is fine - lofting things that go in straight line is easy.
Props and turbines go in circles though, so I have simply "bent" all my 2D stuff around the circumference of it's station out from the hub center.
My PLA 3D print was just for testing that I'm on the right track, which so-far seems yes. I'm planning some Resin prints (47 x 47 x 25 micron resolution on my SLA printer) for small propeller tests (Drones and RC boat props, plus some scale water turbines) and to mill a 125cm prop (or negative mold) for my ultralight - so there's a mix of resolutions I need in all that.
As for "arbitrarily dense point cloud" - yes - my algo is "dimension agnostic"; it's doing 2D right now, but the number of dimensions is arbitrary (1D, 3D, 4D+ all work the same) - I've got 2 solvers:
1) "shape matcher" (feed it anything, say how many control points you want, and you'll get back the best possible arrangement of points to most closely approximate the original shape with an N-dimensional b-spline.). I chose 17 points and 2D foil shapes, but it doesn't care about the shape or dimensions (it does get slow with too many points though...)
2) "fluid analysis" (feed it a sensible airfoil spline [e.g. output of above], your chosen operating conditions, and a method to understand what "best" means*, and it uses gradient-descent (after a bunch of other pre-optimisers) to find it. * "Best" is another spline - so you can pick any arbitrary shape for how you want to genetically engineer the offspring - I've used a bell-curve for my base foils - another example would be "camel shape" (dual bells) for finding the best wing airfoil that works for slow take-offs, and also has excellent cruise performance.)
Once you've got the control points from the above, you can pick any density you want. My goal to try and move to nurbs is the opposite though - to try and only have to use my original 17 points to get a 100% accurate reproduction of the foil that the solver found. I love curves, I hate triangles...