That website tries too hard to write clever marketing copy and does a bad job describing what actually is.

Better description: Pinokio is a free, open-source "AI browser" that simplifies installing, running, and managing complex, open-source AI applications and creative tools (like Stable Diffusion, ComfyUI) with one-click scripts, removing the need for coding or complex command-line setup.

Not sure I would name a product after a legendary liar...

But sure, click that download link, what's the worst that could happen? Get turned into a donkey and swallowed by a whale?

Sweet, thank you for sharing. In my case, I need an api I can call cause i only have a mac air which is essentially worthless for development lol. Also I am bootstrapping a startup and one of the features is essentially turning rooms into 3d space. I know theres matterport 3d and some others but still looking for something simple where i could pay a couple cents per api call with x amount of images. does that make sense?

Surely this is not an LLM?

You could use both. Photogrammetry requires you to have a lot of additional information, and/or to make a lot of assumptions (e.g. about camera, specific lens properties, medium properties, material composition and properties, etc. - and what are reasonable range for values in context), if you want it to work well for general cases, as otherwise the problem you're solving is underspecified. In practice, even enumerating those assumptions is a huge task, much less defending them. That's why photogrammetry applications tend to be used for solving very specific problems in select domains.

ML models, on the other hand, are in a big way, intuitive assumption machines. Through training, they learn what's likely and what's not, given both the input measurements and the state of the world. They bake in knowledge for what kind of cameras exist, what kind of measurements are being made, what results make sense in the real world.

In the past I'd say that for best results, we should combine the two approaches - have AI supply assumptions and estimates for otherwise explicitly formal, photogrammetric approach. Today, I'm no longer convinced it's the case - because relative to the fuzzy world modeling part, the actual math seems trivial and well within capabilities of ML models to do correctly. The last few years demonstrated that ML models are capable of internally modeling calculations and executing them, so I now feel it's more likely that a sufficiently trained model will just do photogrammetry calculations internally. See also: the Bitter Lesson.