Non-linear combination of PDAF information, along with the contents of the images themselves is an interesting demonstration of the power of neural networks. The general concept of non-linear combination is likely how our brains work too.

Given that they built just one testing rig, and they use information like "commonly known object sizes" for depth, I wonder if they are using any transfer learning at all? I imagine whoever is bringing that rig around can't look at all objects, and transferring the knowledge of "common object sizes" and even what an object is can be hugely valuable for such a project.

I bet this is also a sneak peak into how powerful self-driving cars can be at Waymo. Not only do such cars have access to human data (sights, sounds), they can have way more sensors which can combine in a neural network to predict things way better than humans. For example, a NN could infer the chance a pedestrian might veer from the sidewalk into the streets by analyzing:

- The psychological state of a person using a high res camera on the face.

- Using chromographs to see if the pedestrian is drunk.

- Infrared cameras to see the person's temperature / if she's excited, etc.

- Limb modeling and likely walking paths.