No, no, the depth is _inferred_, not hallucinated. See? There's a difference.

Ya, when we specifically ask for hallucinated data we call it _inferred_ data. As I want "good" pictures, I give the system permission to add/remove/infer/hallucinate whatever data it can to achieve a "good" image. Very soon we will all look like supermodels... at least in photos.

Not everyone is a socmed influencer. Some of us want to take pictures that match reality.

In that case, most of the information in every single color digital photo is "hallucinated", because each sensor pixel only captures red, green, or blue, in a checkerboard pattern called a Bayer array; we get uniform RGB photos by applying heuristic interpolation algorithms to (shall we say?) "infer" the missing data.

Stereo cameras/camera arrays can have very large resolutions. An ML model is necessary for optimal results with stereo cameras, but it's more grounded in reality and verifiable versus a mono capture. With a large enough camera array, you can get precise, high resolution depth without ML, especially if you accept Gaussian splats, which use gradient descent but I wouldn't call them generative models in the same sense. Large, featureless, textureless planes are still a weakness however, while generative models can guess/interpolate values.

You can use a hybrid method with speckle projection/ToF/LIDAR (the first two behind mainly usable indoors) if you want to stay fully grounded though.

Phones (Google Pixels at least) use sensor data from inertial sensors and slight hand movement as part of their depth calculation: https://research.google/pubs/depth-from-motion-for-smartphon...

It looks like this research applies specifically to AR video streams, leveraging motion to combine data across frames.

I do not believe this technique is used for photographs.

> I draw the hard line in the moment its not deterministic anymore

Photography never was. It has been copy&pasted together from multiple exposures, different photos and such in the darkroom for well over a hundred years. And once digital became common place the manipulation went completely into overdrive. The photo you see has little to do with the photo that the camera captured.

If we cared about fully deterministic captures, we'd all be using Lytro lightfield cameras, stereo cameras or 360 cameras or other contraptions that capture more light information than the classic camera.

Is "deterministic" really the right line to draw? On the one hand, this is "deterministic", in the sense that (given a fixed model) the same photo will produce the same depth estimation. On the other hand, even film isn't deterministic, with its random grain. But at least random grain looks random. Perhaps the issue at play is really "indexicality", where perceptible signal should be caused by the scene. But even that is fuzzy. What, qualitatively, is the difference between "touching up" and AI enhancement? It's all just algorithms that combine a starting photo with some knowledge of how humans like photos to look. Again, not even film is free of this - when chemically developing, the choice of how strong a developer to use and how long to leave it in has as much effect on the image as the exposure settings chosen by the photographer. There's nothing "deterministic" about a lab technician eyeballing your photos and tweaking the development process.

As a small niche like color key or IR photography or anything else everyone else did once :D