"To deal with textureless regions and cope with low-light conditions, we make use of an “active stereo” setup, which projects an IR pattern into the scene that is detected by stereo IR cameras. This approach makes low-texture regions easier to identify, improving results and reducing the computational requirements of the system.
Stereo sensing systems can be extremely computationally intensive, and it’s critical that a sensor running at 30Hz is low power while remaining high quality. uDepth leverages a number of key insights to accomplish this.
we apply a novel technique for neural depth refinement to support the regular grid pattern illuminator on the Pixel 4. Typical active stereo systems project a pseudo-random grid pattern to help disambiguate matches in the scene, but uDepth is capable of supporting repeating grid patterns as well. Repeating structure in such patterns produces regions that look similar across stereo pairs, which can lead to incorrect matches. We mitigate this issue using a lightweight (75k parameter) convolutional architecture, using IR brightness and neighbor information to adjust incorrect matches — in less than 1.5ms per frame."
In the end, Google gives a couple of examples of depth-enabled photography features:
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