> Multiple images can be used to compute a 3D point cloud. [...] Add a GPU enabled system to process (it's quite compute heavy) and you are set.
It requires enough information ("features") in the images to compute a stereo pair. Flat patches of color have no features and as such, cannot be correlated between cameras. In such a case, you have a spot with no depth information.
This is exactly why you want to have other sensors, and saying that "oh humans have two eyes and they do fine" doesn't really cut it. Humans can say "hey this flat patch of color is a sign and signs are not dangerous" or "hey this flat patch of color is a really clean semi truck, and crashing into trucks is bad" but computers aren't that smart.
> Flat patches of color have no features and as such, cannot be correlated between cameras.
Flat patches of color are also flat, which makes it possible to fill in the missing depth information.
I took a course in computer vision where one of the projects [1] involved monocular vision, and assuming that flat patches are flat, vertical lines are vertical, all others are horizontal and the background is flat, it was possible to get a pretty good reconstruction.
Everything has texture, even things that appear solid, this is how optical mice can work on glass. Throw in NIR/UV camera, or maybe thermal for good measure, slap LSTM on top and you are covered for everything human would spot.
It requires enough information ("features") in the images to compute a stereo pair. Flat patches of color have no features and as such, cannot be correlated between cameras. In such a case, you have a spot with no depth information.
This is exactly why you want to have other sensors, and saying that "oh humans have two eyes and they do fine" doesn't really cut it. Humans can say "hey this flat patch of color is a sign and signs are not dangerous" or "hey this flat patch of color is a really clean semi truck, and crashing into trucks is bad" but computers aren't that smart.