Sure, but isn’t the mapping completely known from the math? I mean, I don’t think this mapping problem is some complicated “find solutions that satisfy a non-linear black-box.” My understanding (based on homogeneous coordinates (ie: non-uniform weighted NURBS)) is that we know what is in the box, and we have NURBS machinery to handle it.
Mappings between rational and non-rational spaces are why NURBS can exactly represent conics, but non-rational splines cannot.
Isn’t this only a problem if one were to insist on using non-rational splines (for some things but not other things)? And if the answer is yes, then it is an interesting question as to why are we insisting on this?
I imagine there are good answers to this question, however they may be difficult to explain, or even undesirable to explain. Because, in some sense, the answer could be,“To make our algorithms fast and robust we gave up some accuracy.”
The analogy would be “good, fast, cheap, only pick two out of three.” And then there is always someone (like me?) who complains about the missing third quality.
But honestly, I’m good with an answer like that, so long as I’m told that “As a design choice, we picked these two out of the three. The tradeoff was difficult, but this is what we decided.”
For me, my special-case is very simple, and I am only dealing with 2d curves. So the mapping is exceptionally simple. It could be that even this simple mapping gets complicated on intricate 3d breps and tight tolerances. I don’t know, I’m not an expert in this area. But I would be OK if some expert said,“I can’t easily explain why, but we chose these particular trade-offs.”
P.S. A minor technical note:
Non-uniform Rational B-splines are more general than non-rational B-splines. More precisely, non-rational B-splines are a subset of non-uniform rational B-splines. So mapping from non-rational to rational is easier than the other way around. So isn’t “not rational 2d space” mapping to “rational 3d space” the easy case?
Also, when the distortion is exactly known, mathematically, and the distortion is invertible, then we usually just correct for the distortion, right? The only problem is if the inversion leaves the space of solutions that we are restricting ourselves to using (or if the inversion is numerically unstable). But then, that restriction is by our own choice.