I suppose the stereoscopic vision system could use NN's, but the implementation can be swapped for something else as long as the result (interface) is a depth map. You could switch it from LIDAR to multi-eye stereoscopic translation and vice versa. The interface between the parts is something human analysts can inspect and relate to.
You may also have a 3rd module that attempts to model the scene using 3D rendering of the "guessed" objects (using a database of known objects) from the flat-image NN and depth-map first pass, using the match/non-match feedback to test yet more guesses. (Certain objects with semi-random shapes like a pile of cloth or mountains may be excluded from known-object-matching, at least as a test candidate.)
There might even be a genetic algorithm to "breed" the best 3D-rendered model that fits the depth map, the flat image, and the top flat-NN candidates.
> A more radical possibility is to give up trying to tackle the problem at hand by training just one big network and instead have multiple networks work in tandem. In June 2018, the DeepMind team published an example they call the Generative Query Network architecture (7, http://science.sciencemag.org/content/360/6394/1204?ijkey=01...), which harnesses two different networks to learn its way around complex virtual environments with no human input. One, dubbed the representation network, essentially uses standard image-recognition learning to identify what’s visible to the AI at any given instant. The generation network, meanwhile, learns to take the first network’s output and produce a kind of 3D model of the entire environment—in effect, making predictions about the objects and features the AI doesn’t see. For example, if a table only has three legs visible, the model will include a fourth leg with the same size, shape, and color.