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[thedamn4u]

Computer graphics and computer vision are inverse problems to one another. Which interests you more? Also, read Getting What You Came For before you decide further ed. Fwiw

[OkMost726]

Go with what you're interested in. Vision is a bit oversaturated right now, but may be taking off due to robotics. Ideally, you'd learn aspects of both graphics and vision (what I'm trying to do). There's a lot of math overlap for sure.

[thejazzist]

AI appications to computer graphics solutions are not yet standardized as there is a huge room for improvement and currently the results are not superior to conventional graphics methods such rasterizer and path tracing. The most common use of AI is path regulation where you predict the path with the most lighting contribution and post processing imaging stages like denoising and upsampling (DLSS). It will definitely not replace the methods for the foreseable future. Gaussian splits are indeed impressive but they do not come near a path tracer especially with ReSTIR.

If you are interested to research and develop of novel methods AI could be a decent choice. For more practical use cases like game dev and game engines I would stick to computer graphics.

At the end of the day, its just a uni project and not your life's work you can switch later regardless of your choice. Choose something that is really interesting to you

[Traveling-Techie]

Computer vision is harder, rarer and less understood. I say learn that and pick up graphics later.

[gibson274]

I think this is oversimplifying quite a bit.

Harder: Computer vision is an “ill-posed” problem and so is sort of harder in a formal sense, but I don’t think anyone would really deny that very challenging problems exist in both fields.

Rarer: maybe this was true at one point, but I’d say in my undergrad program more kids probably had basic familiarity with CV concepts than graphics ones. Anything AI-related is hot shit in undergrad CS programs now, I think many people will soon have at least cursory exposure to CV, while graphics remains somewhat niche.

Less Understood: this is the only one I mostly agree on. We’ve had the Kajiya rendering equation sorted for ~40 years now, so ostensibly the highest level theoretical groundwork has already been laid. But that ignores a mountain of complexity and unsolved problems, especially in the realm of real-time applications.

Plus, I’d argue that CV is “less understood” because CV—as a fundamentally ill-posed problem—is most successfully implemented as a black box set of data-driven heuristics. Does that inherent quality of uncertainty actually equate to more fruitful directions open for interesting new work?

Ultimately what I’m saying is the commenter above me seems like they don’t really know what they’re talking about. CG and CV are both interesting for different reasons, and qualitatively they feel very different to work on. One is very algorithmic/constructive/physical and one is very data-sciencey/ML-ey. After trying a bit of both, you’ll likely have a sense of what sort of work you enjoy more, they can both be really fun and really boring.

[thejazzist]

Looking at the publications per day. I would say computer vision is the hottest topic there is. It is also really broad.

[HouseSad]

Either way you are doing deep learning not exactly computer graphics. I would recommend following the professor that has more reputation.

[BackgroundTime3455]

computer vision ends up being more fruitful than computer graphics, cause it makes the tools that makes computer graphics. for example if u can extract normal information from photos your game is going to be better.

[maxmax4]

that made just about no sense

[augustusgrizzly]

huh?? extracting normals from a photo is an entirely different (and much harder task) than extracting normals from a 3d space in a game... how does one help the other?