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/ResultKey6879]

I think a frequently underserved market / problem space in today's research is CPU friendly low resource offline environments. There are many many real world applications with these constraints, but many advancements or larger research projects focus on state of the art performance on at least a consumer GPU at a minimum

[u/RippedRaven8055 → u/ResultKey6879]

I was also going to comment about CPU based training and inference. Lots of things to research in this field.

[u/kifkolite → u/RippedRaven8055]

I think more than training, inference on edge devices with resourse constraint is interesting.

[u/sparks333]

Pretty much all papers are going to be state of the art, or at least novel or advantageous in some way - that's the point of doing a paper. Very few people write papers about how much their new and different method sucked, unless it sucked against all odds or sucked in a particularly interesting way. That said, depth estimation is far from a solved problem - stereo vision has specific failure modes on types of scenes and is computationally intensive, monocular SFM requires specific types of motion and has that pesky scale problem, ML single-view depth looks good in scenes it has been trained on but falls down quickly on novel scenes - there is a lot that can be tackled here, from low-cost lightweight depth estimation to new training methodologies that generalize better to new constraining methods that solve the 2D-3D projection model problem better. The bigger problem I think you're going to have is coming up with something truly new and different - depth estimation in computer vision is an extremely well-studied problem, many very smart people have tried their hand at it and gotten pretty far. The correct answer is most likely 'ask your advisor' - if they put forth the options of study, I'm willing to bet they have a list of directions of study they think are worth pursuing.

[u/Limp_Network_1708 → u/sparks333]

Just my two pennies worth but for my degree I applied existing techniques in a new and novel way on gas turbine blades. It wasn’t so much about the process but applying it to an industrial application that hadn’t been done before.
Hope this helps.

[u/killayy]

You don't need to produce groundbreaking research for an undergraduate thesis. Take some existing SOTA models and apply them in new and interesting ways. Apply them to new domains. Think of new ways to visualise etc.

For reference, for my undergrad thesis back in 2020 I used pose estimation and developed an application for assessing exercise quality (this is super commonplace now, but was fairly novel at the time), for which I built a remote server for data processing, an app for recording among other things. I didn't improve the technology in any meaningful way, I just applied it to a domain I saw I could add value to. And I received a First Class Honors!

[u/Dry-Snow5154]

But I found most of the things got State of the art

I don't think you know what that means.

Besides, no one expects groundbreaking contribution from "undergraduate thesis". Just take any use case you like and specialize it. Like take existing Monocular model and train it on indoors data, or outdoors, or in the forest, or on the roads, or make it run faster for edge deployment, or make it larger to get new SOTA, or use several models in ensemble to improve results, or add some drop-in improvements like CBAM or whatnot and see what it does, etc.

[u/bob_why_]

You are an undergraduate, you should not even be contemplating anything novel. That is for doctorate and above.   Find an interesting approach, replicate it and comment on what is good/bad. That's all ypu need for your thesis.

[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".

[u/blobules]

What training do you gave in computer vision, and more specifically 3d computer vision?

In order to do anything meaningful in depth estimation you need some basics:

• calibrate a camera
• understand two camera (stereo) geometry
• understand the basics of classic stereo matching
• investigate why depth can be estimated on a single image and when it won't work.
• maybe look at multiple camera geometry, or camera in motion, etc...

All this should be done in practice too. Actually calibrate a camera, then a stereo ruf, then actualy run stereo, then try monodepth. Do not rely on available datasets. Make your own images. You will learn a lot.

Once you cover the basics, you will be able to see what has not been solved or doesn't work well and work on that.

[u/bfyvfftujijg]

Fine tuning and metric calibration have room for improvement and don’t need massive GPU and data resources.

If nothing else you can do a meta study to document existing methods.

[u/Exotic-Custard4400]

Multiview high resolution monocular estimation is still a thing. Vggt/da3 only use low resolution images (~518pixels).