[electronvolt]

I think you're kind of missing the author's point.

> My feeling is that the "perfect" abstraction of reality to geometry is actually a very high order function that we don't fully understand.

You don't need a perfect abstraction of reality, though. All you need to do is get close enough to catch up with humans or outperform them and you've solved most of the hard problems in computer vision. Fortunately--at human scales, which are the only ones you need to care about for computer vision, reality behaves closely enough to a pure Euclidean space that you're fine. :)

The author's primary argument seems slightly more nuanced, too, than just "It'd be nice if we could use old techniques".

Basically, they're claiming that if you can build geometric understanding into the ML model, you will get significantly better results that just naively plugging and chugging away with raw data. That's an empirical claim that can be validated by researchers--either it will give a significantly improved performance on well defined problems (stereo vision, etc.) or it won't. Vision is one of the research areas that have developed pretty good benchmarks over the years. :)

[taeric]

But that is exactly my point. Geometric models are the old tools we had. The new tools are ML Models. It would be nice if they both worked together. But there is nothing to say that they should. Nor is it obvious that it would be beneficial.

Your point that this is testable, though, is important. I fully agree with that and was not intending to dismiss the idea. Just because I am not as confident as the author, does not mean that I am right. :) (I'd accept that I am likely not right.)