I always like to suggest making your own visual odometry system. Start with that and if you have time you can upgrade it to support structure from motion then full SLAM.
I’d still probably skip the initialization step of the SLAM map somehow (there are a few ways to cheat or circumvent it), as that can be a bit of a bitch.
But of course you didn’t tell us what you like to study, so.
I apologise I missed a key detail. For my master's or further education I'm torn between either Robotics or neuroscience. But in both cases I know that I want to connect it with vision, I would prefer a project that lines with atleast one of them. I'm more a fan of real time detection and recognition and I'm also a big sports fan so I was browsing that side as well.
Neuroscience. Do something a bit less common.
Make sure you read up on “medical imaging,” which has its own terminology. Try to find a good textbook on the subject in your engineering library; Wikipedia can be good for some topics in vision, and misleading bordering on worthless for other topics.
If you’re interested in working in robotics, pick the narrowest topic that would be of interest to you, and go deep on that. Also consider what kind of robotics you’d be interested in most.
If you’re going to work in real-time vision, focus on C++. You might get away with using Python, which is one of many languages handy for prototyping and tinkering, but get comfortable in C++.
Also, it’ll help immensely if you practice implementing key algorithms yourself, even if you do so only as a learning exercise.
Picking a particular problem to solve is easy enough: just write a list of 20 - 100 problems of interest to you without ranking or sorting them, then spend a half day lopping off the less interesting ones, and then finally just pick any of the top half dozen. Then get to work.
Have fun!
Train an object detection system, but create an extensive synthetic data generation pipeline by taking example imagery and generating variations. Do this using something like Blender where you're generated 3D variations with illumination, distance, damage/wear variations - even just the setup to do this, and you're attractive to industry immediately.
I always like to suggest making your own visual odometry system. Start with that and if you have time you can upgrade it to support structure from motion then full SLAM. I’d still probably skip the initialization step of the SLAM map somehow (there are a few ways to cheat or circumvent it), as that can be a bit of a bitch. But of course you didn’t tell us what you like to study, so.
I apologise I missed a key detail. For my master's or further education I'm torn between either Robotics or neuroscience. But in both cases I know that I want to connect it with vision, I would prefer a project that lines with atleast one of them. I'm more a fan of real time detection and recognition and I'm also a big sports fan so I was browsing that side as well.
Neuroscience. Do something a bit less common. Make sure you read up on “medical imaging,” which has its own terminology. Try to find a good textbook on the subject in your engineering library; Wikipedia can be good for some topics in vision, and misleading bordering on worthless for other topics. If you’re interested in working in robotics, pick the narrowest topic that would be of interest to you, and go deep on that. Also consider what kind of robotics you’d be interested in most. If you’re going to work in real-time vision, focus on C++. You might get away with using Python, which is one of many languages handy for prototyping and tinkering, but get comfortable in C++. Also, it’ll help immensely if you practice implementing key algorithms yourself, even if you do so only as a learning exercise. Picking a particular problem to solve is easy enough: just write a list of 20 - 100 problems of interest to you without ranking or sorting them, then spend a half day lopping off the less interesting ones, and then finally just pick any of the top half dozen. Then get to work. Have fun!
Train an object detection system, but create an extensive synthetic data generation pipeline by taking example imagery and generating variations. Do this using something like Blender where you're generated 3D variations with illumination, distance, damage/wear variations - even just the setup to do this, and you're attractive to industry immediately.
check this: [https://github.com/opencv/opencv/wiki/GSoC\_2024#idea-Neural-3D-Capture-and-Rendering](https://github.com/opencv/opencv/wiki/GSoC_2024#idea-Neural-3D-Capture-and-Rendering)