Why not use synthetic datasets for training machine vision (deep learning) algorithms?
Well, when you're training something against a dataset, you're training it to work on that kind of data.
Since we have no idea of what the neural network &quot;uses&quot; from the input images to fulfills its purpose, these might be things where our computer graphics aren't quite like real-world images.
We're &quot;trained&quot; with real-world imaging, perceived through our eyes with our brains. The neural net has none of that. It might simply do the segmenting, and that's actually pretty likely, on the specific kind of anti-aliasing done by the 3D renderer. Great, now you have a neural net that can segment generated images very well, but can't deal with real-world photography. Not quite sure what you've gained there!
But: sometimes, you actually do that, if you can describe your real-world data well enough – but that's usually things like RF signals, abstracts datasets etc., and often you'd intentionally add noise, so that the noise equivocation is more prominent than the differences between synthesized data and the real world.
You'd always have to verify, either way.