Okay, 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.
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.
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.
Lol. The author seems to think that people only deserve privacy when in color. Faces are blocked in the original color image but not in the black-and-white depth image.
Is it just me or does the depth map also include color/brightness? The vatograph images shown seem to also be mostly about subject recognition, not depth. How accurate are these inferred depth maps?
It's not accurate at all. Measuring accuracy would require some sort of calibration. If this was using some kind of stereographical setup or lidar data to calibrate the inferred depth could probably be calibrated, but this is not the case.
Not to say it's useless, its perfect for subject isolation and it would be very difficult for someone to tell the difference with a scientifically accurate equivalent
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.
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.
Not everyone is a socmed influencer. Some of us want to take pictures that match reality.
https://news.ycombinator.com/item?id=44183591
More things on there about ways to capture depth maps instead of generating them from a single image
Not to say it's useless, its perfect for subject isolation and it would be very difficult for someone to tell the difference with a scientifically accurate equivalent