If you don't have to design for assembly, you aren't dealing with hardware. The fact that software essentially has zero reproduction and distribution cost is what makes it so much easier.
Put it this way, when you buy a car, the difficult part for the manufacturer wasn't the customer-experienced design of the car, or even the driving concept. The hard part is designing the assembly line, and designing the car in such a way that 100,000 of them can be put together such that the assembly of each individual piece takes exactly the same amount of time, otherwise there are assembly line stalls. Then you also have to design, or at the very minimum optimally position and program, the machinery to put the pieces together, organise supply chains, deal with subpar contractors whose quality changes even if they are delivering theoretically the same product, etc.
Seriously, I have done my time in the CV space, and it is a lot easier than real world hardware, even if CV is still being explored, just because of the distribution and replication of a finished product.
I don't disagree that hardware itself has more dependencies. I don't think that makes it harder or less probable of delivering though than bleeding edge computer vision/machine learning systems.
I too have worked hardware and on those projects we could get fairly low level line workers on assembly up to speed quickly, source our parts in a repeatable fashion and build systems to scale without too much brain power. Not easy, but more logistics management than creativity.
With these non-deterministic CV systems (for example point cloud generation) the path to working is much less clear.
I'm not saying one is harder than the other, but the class of unsolved problems in CV/ML doesn't have off the shelf solutions, so they pose different but similarly hard problems as hardware.
I still think ml systems, which I work on, are easier than hardware, which I used to work on. It's things like the cost to dump inventory if you make a mistake (eg a founder cheaped out on a ballast circuit on a dna reader, which caused the light to flicker at power on, which fucked up the chemistry, which cost the company well over a million dollars), etc. Which is not to imply novel ml systems are easy...
Pair3d looks really cool -- I've been making a list of neat industrial uses of computer vision tech and virtual showrooms isn't something I'd thought of.
A friend actually did something similar the hard way -- he and his fiance were apartment shopping in nyc, but the apartments were empty and they were having a hard time visualizing what they would be like (less spacious, for starters) with furniture. So they cut butcher paper into pieces the size and shape of their major furniture and laid it out in the apartment to see what it would feel like with their couch, bed, etc in the apartment rather than empty.
A lot of the CV problems are essentially 'solved', though.
Feature extraction (AKAZE, BinBoost), matching (GPU brute force hamming), RANSAC (with PROSAC and relatives), bundle adjustment (Schur Decomposition with LM), point clouds (SGM, PatchMatch) and mesh (Poisson, FSSR).
Each stage has tuning, and real-time requires sacrifices on the hardware we have available, but we know with better computers we can have it. (HSA has me drooling, hurry up with Zen, AMD!)
IMO the harder stuff is in semantic segmentation of point clouds and dynamic scenes, but I have high hopes for the next few years.
I feel like it should be easier than images, since we have all the 3D information. There are all of the features based on 3D structure which image segmenters can't even begin to use.
It's one of those things that we can do efficiently, so with enough priors of what scenes look like a sufficiently informed ML system should be able to get decent accuracy.
There are whole fields more or less dedicated to it! In mathematics, queueing theory; in engineering pretty much the entirety of Industrial Engineering; and there are probably 10 competing frameworks in management, the most important being Lean, Theory of Constraints, Six Sigma.
Put it this way, when you buy a car, the difficult part for the manufacturer wasn't the customer-experienced design of the car, or even the driving concept. The hard part is designing the assembly line, and designing the car in such a way that 100,000 of them can be put together such that the assembly of each individual piece takes exactly the same amount of time, otherwise there are assembly line stalls. Then you also have to design, or at the very minimum optimally position and program, the machinery to put the pieces together, organise supply chains, deal with subpar contractors whose quality changes even if they are delivering theoretically the same product, etc.
Seriously, I have done my time in the CV space, and it is a lot easier than real world hardware, even if CV is still being explored, just because of the distribution and replication of a finished product.