We're using the camera for an autonomous toy car racer, so I need reliable and real-time depth estimates. Existing cameras such as the Stereolabs ZED max out at 1080p @ 30 fps and they use rolling shutter which isn't even perfectly hardware-synchronized. Plus those sensors are tiny and, hence, as noisy as a laptop webcam.

The result is that the Stereolabs AI needs to be extremely lenient when doing the stereo matching because objects will almost never look exactly the same in both images, be it due to the noise or the rolling shutter skew. If I see a pattern repeat itself on both images with 5% RGB intensity, then on the Stereolabs ZED I need to ignore that, because it's most likely just sensor noise. If the image was almost noise-free, then I could treat this pattern as a reliable correspondence and triangulate depth from it.

Also, tracking fast movements at 30 fps is really difficult, due to the large movement offsets. If you scan for them, you need lots of compute power and you risk recognizing repetitive patterns as fast movement.

If you increase the hardware from 1080p to 4K, from 30 FPS to 120 FPS, from "really noisy" to "practically noise-free", and from "rolling shutter" to "hardware-synchronized global shutter", then suddenly you have 4x the data to make a decision on, all your offsets are 4x smaller due to higher FPS, and you can treat much weaker patterns as reliable.

And all that together means that surfaces like reflective wooden floor are now doable. Whereas before, most of the visible patterns would drown in sensor noise.

EDIT: And maybe one more comment: Our camera uses USB3 10gbit/s with a high-speed FPGA and it was completely designed in the excellent open-source KiCad. I even forked it to make things look nicer and more like Altium: https://forum.kicad.info/t/kicad-schematics-font-is-a-deal-b...