Research is additive not a zero sum thing.
lmao are you for real?
From what I understand, they use the text keywords detected from the image as guidance and they also apply a loss between the current diffusion state and the source image. In effect, this is stable diffusion for 3D shapes but with clever conditioning. That means this algorithm will also work just fine if you have 2+ input images.
The more assumptions you can bake into the parameters of some model, the more degrees of freedom you get in the actual measurement process (e.g. reducing the amount of actual data necessary).
But yes, the images totally came from a controlled environment. They rented like 50 similar cameras and hardware-synchronized the shutters.
What would really make a difference if you could make a decent model from 30 images snapped by a drunk teenager on their $200 budget phone in bad lighting.
If money and resources are not an object then yeah it's easy but for most people it is.
The best camera is the one you have with you, and the same goes for this too.
It's not there yet for AAA games, but will get there in time. In the meantime, right now as it is, it's could save days of time for the indie game dev.
Seems valuable to be able to generate 3d models from single 2d illustrations, for games and other media.
And there are probably heaps of other applications as well.
This is just for creative/artistic use, because you obviously don't want software to "guess" on something used for engineering. In that case, you would use an appropriate 3D scan.
Also, 3D assets aren't generally created as scenes. A 3D-2D-3D pipeline also has wide application in AR, VR, standalone modeling, animation, etc.
The problem with the multi-perspective approaches are: to do it well you need lasers and extremely stable AND perfectly localized observation points, making it slow, expensive and fragile for robotic applications, OR the image-to-model component of it is a grotesque anti-physics artifact hallucination which reinforces entropy as much as it does valid data leading to shitty models.
Essentially, this single image to 3D step is the key to both forms.
To effectively mirror this process in machines, we need models that can extrapolate a 3D environment and it's objects from sensors. If we can get an approximation of what something looks like from a different perspective, it's valuable for naviatigation and objective planning.
We could also theorize about the effects it will have on machine's awareness and attention.