31–40 of 43 posts
Its funny, always stucks on 90% till it fails with the error that another big image may be keeping the server busy. I mean ok its a "demo" tho the funny thing is if you actually check the cli and requests, you clearly can see that the 3 stages the images walks through on "processing" are fake, its just doing 1 post request in the backend that runs while it traverses through the states, and at 90% it stops until (in t…
If this model is so good at estimating depth from single image, shouldn't it also be able to take multiple images as input and estimate even better? But searching a bit it looks like this is supposed to be a single image to 3D only. I don't understand why it does not (can not?) work with multiple images.
Cool, is there a way to upload several photos of a room from different angles to fuse it all together? Is there an api?
That's a pretty well-solved problem at this point, if you want to do it yourself. You'll want some kind of NeRF tool and a way to calculate the camera poses of the photos you took. COLMAP is the tool most people use for the latter. I'd recommend trying Instant Neural Graphics Primitives ( https://github.com/NVlabs/instant-ngp ) from NVIDIA. It's a couple years old, so not state-of-the-art, but it runs on just about a…
If this model is so good at estimating depth from single image, shouldn't it also be able to take multiple images as input and estimate even better? But searching a bit it looks like this is supposed to be a single image to 3D only. I don't understand why it does not (can not?) work with multiple images.
Stuck at 90% forever..
If this model is so good at estimating depth from single image, shouldn't it also be able to take multiple images as input and estimate even better? But searching a bit it looks like this is supposed to be a single image to 3D only. I don't understand why it does not (can not?) work with multiple images.
If you have multiple images you could use photogrammetry. At the end, if you want to "fill in the blanks" llm will always "make up" stuff, based on all of its training data. With a technology like photogrammetry you can get much better results, therefor if you have multiple angled images and dont really need to make up stuff, its better to use such
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.
If this model is so good at estimating depth from single image, shouldn't it also be able to take multiple images as input and estimate even better? But searching a bit it looks like this is supposed to be a single image to 3D only. I don't understand why it does not (can not?) work with multiple images.
Stuck at 90% forever..
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