glasses VS monodepth2

Yes, they are. Take with a grain of salt, but researchers (usually) do not know how to code and (or) they don't care to properly share their work. Things that are learned in the first Computer Science bachelor year, like OOP, DRY, packages, good variables/function naming, are apparently not used in ml research. This is why I created my own library (https://github.com/FrancescoSaverioZuppichini/glasses), for me, good code means less time I have to spend working and more free time.

I have implemented most of the sota models in my library (https://github.com/FrancescoSaverioZuppichini/glasses). These are my 2 cents:

I've used monodepth2 before and it's great: https://github.com/nianticlabs/monodepth2

How can a single camera do that? My experience with computer vision is fairly limited so I'm curious how that would work. My understanding is you need to be able to generate a point map, stereo vision, or some non-CV related method , e.g. radar like the pixel 4. 2D depth estimation can be done with a single camera in somewhat useful way but it's not a secure way (https://github.com/nianticlabs/monodepth2 -- now somewhat similar functionality in OpenCV). Can you expand on what AI the single camera is being combined with that provides security guarantees?

I suspect you may have the best success by using monocular depth approaches (for example something like this: https://github.com/nianticlabs/monodepth2).

After that I have to do the same for Python I think, and then I have to find out how to figure out how to use a library like https://github.com/ialhashim/DenseDepth or https://github.com/nianticlabs/monodepth2 for that GStreamer plugin (or element, still trying to grasp the terminology here)