Your stationary plane example highlights a divide I've seen across my work experience in different domains; teams defaulting to ML when fundamental engineering would work better.

I'm curious: do you think there's any amount of high-quality data that could make the learning-based approach viable for orientation estimation? Or would it always be solving the wrong problem, regardless of data volume and delivery speed?

My sense is that effective solutions need the right confluence of problem understanding, techniques, data, and infrastructure. Missing any one piece makes things suboptimal, though not necessarily unsolvable.