alt Hacker NewsOkay, I see where this is going and I guess as an seperated art that uses a photo as start it is okay. In a photographic sense it is not, its not better than any given smartphone hallucinating depth of field when the mono-aperture lense does not has it.
As someone who has a photography walk every few months to years I draw the hard line in the moment its not deterministic anymore, and if I understand this right..its some kind of machine learning modell interferring the depth by looking at it. The data isnt really there, in short, the depth is hallucinated. Same when you use KI fill to touch up your photos...changing exposure, even in zones, boosting colors from raws, all fine..but the moment you non-deterministicly touch a photo it ceases to be a photo and starts to become an AI image that happens to have a photo as starting point.
That all said, on this very topic, is there a camera that actually captures distance in any meaningful resolution? There are fun ToF sensons and 3d voxel clouds of places, but those are usually not something you can snap in milliseconds.
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No, no, the depth is _inferred_, not hallucinated. See? There's a difference.
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.