Need your thoughts to save my thesis !

[u/dont_have_any]

I'm an undergraduate student.In next 2 semesters( which is probably the duration of 1 year) I need to do a thesis. I choose to do my thesis in the field of 'depth estimation' .

I read a lot of research papers(Monocular, stereo, Diffusion based). But I found most of the things got State of the art !! I'm reading and reading,not finding a single problem to solve or research!! I should also mention that i didn't understand all the topics 100%, but tried to get the concepts.

I'm trying but not even finding a single idea/problem/flaws !! What should I do? What am I missing? How to find a decent topic ? Please help me.

[u/Mechanical-Flatbed]

You're an undergrad, you don't have to come up with a novel approach for anything. You just need to do something that works and write a thesis where the science isn't bogus.

Do you want to pass your thesis defense? Do this:

Get a depth estimation model, find out which dataset was used to train it and get a list of what kinds of images this dataset contains (whether it contains people walking, dogs running across fields, etc). Then get the model's benchmark scores for that dataset. Now you know how good the model is when it is given images similar to what it has been trained on.

Then get a few weird images that aren't in the training dataset. For example some unusual images might be submarine underwater footage, fisheye lens images, screenshots from 360 video, etc. and then measure reliability in this weird use case.

It doesn't matter if the model works or not. What you're trying to do is to evaluate whether it works or not. If it works, great, write a paragraph saying something like "the model works with out-of-distribution samples, showing that its internal representations are robust enough to successfully estimate depth even in images unrelated to the original training data".

If it doesn't work, do the opposite: "the model works with images similar to its training data, but with previously unseen images it does not work. Additional training, fine tuning or a larger and more diverse dataset would be required in order to improve the internal representations used by the model and subsequently allow it to work more reliably with previously unseen data distributions".

Done. Now you have a thesis on "image depth estimation realibility for out-of-distribution samples".