robertleoj

Hello Dima!

Thanks for replying - I really admire your work with mrcal, and have used it for a while in my work. And lensboy is heavily inspired by mrcal.

> Is your splined representation compatible with the mrcal splined stereographic model?

No. It was a conscious design decision to only use pinhole models, at least to begin with. As I understand it, the tradeoff is that the pinhole model does not support ultra-wide lenses like the stereoscopic model does. But I have never used a lens that has a field of view close to the limits of pinhole models, so I just went with pinhole models.

However, if I understood your documentation correctly, the distortion model should be identical, it's just the core model that is different.

> Is your splined lens representation better in some way?

I'm pretty sure it's not - I based it entirely off yours, without trying to improve it. However, I did implement regularization slightly differently.

> I didn't see any documentation about it.

Unfortunately I have not written documentation as impressive as you have in mrcal, though I aim to. I wanted to get the project working as soon as possible to use it at work.

> If you think the mrcal distribution methods could be improved, and are willing to help improve it, I would be very amenable.

I actually did attempt this in this fork: https://github.com/Robertleoj/drcal (I'm sorry for the brazen phrasing in the readme, but I believe it is sligtly in the style of the name "numpysane").

The purpose of this was just to have functionality of mrcal but available on pypi, and with a build system I understand better.

However, I abandoned this, instead opting for making a library from scratch, partly because I had a hard time removing numpysane as a dependency (I want to have very minimal dependencies in this project). I'm not sure how to reconcile this, but I'm happy to chat about what I did there.

> Do you support mrcal .cameramodel files? If not, can you do that?

I don't support them currently. I'd love to chat about doing this, even if loading them would lose some information because of different data models.

Don't hesitate to send me an email, you can find it on my github profile.

Hello! I'm the creator of SlamDunk, and I use it daily in my day-to-day work for 3D visualizations.

I really like it, so I thought I'd share it.

Did you try using the previous configuration as an initial guess to the numerical optimizer? If the next position is close to the previous one, the solver should be very quick.

Also, you can probably get much faster results if you obtain the gradient of the forward kinematics.

Thanks! Exactly - I feel much more comfortable talking about the hardware at work now after having made this and feel more confident about making something more advanced.

This is my first printer so I don't have anything to compare it to, but to me it seems to "just work"

Right? I'm thoroughly enjoying that rush :D

Really cool 4dof robot - thanks for the reccomendation

Thank you, great to hear!

That's true - using numerical optimization was just the simplest/fastest way to get it working :P

Nice - did you just chuck it into wolfram alpha or sympy? How'd you get it?

Thank you! I made it like that purely for fun, this project was not intended to be useful, or have the best mechanical design :P

Using Gcode is a good idea, though it does seem a bit overkill for this simple application

Oh wow that is refined indeed - I'll check it out, thanks!

That's very inspiring for me to hear, thank you! Glad it's useful.

"Mastered" is a strong word, but thanks!

Hahaha wow that is scrappy as hell - it does look difficult to get right :P

Well in what context does it matter?

If it's useful for you to learn, then it matters, right?

There are also endless applications for hardware, but I guess to make a viable product you probably need a sizeable team of people.

Yeah - I'm using mediapipe for hand tracking, which is straight out of the box. Then I can just make the robot draw the exact sequence of dots my index finger drew :)

Great to hear that it's useful!

Happy to hear :D

Previously, I worked at a startup where we were making a vision system for golf simulators to analyze the player's swing. It's this company: https://www.elvagolf.com/

I learned a lot of computer vision skills from that job, and it turns out this kind of computer vision is directly applicable to robotics as well.

So, I applied for a role as a vision engineer at my current job, and got the role :) This is where I work now: https://www.monumental.co/

Apart from my vision skills though, I was also just generally a good fit - I had a lot of startup/small company experience, I'm a pretty good software engineer apart from the computer vision, and the culture fit was there as well. I feel very lucky that the stars all aligned for me to be here.

Thank you!

I'm an ML engineer early in my career. I'm looking for a position in the US or EU at a company with interesting tech, and a meaningful mission that accelerates human progress. I specialize in computer vision. I've been working on a computer vision for real-time analysis of golf swings, with calibrated cameras in a golf simulator.

In my previous job I made made models for multiple business use cases, such as demand forecasting.

I've only worked in small companies where, in addition to the AI, I've had a wide range of responsibilities, so I have experience with many technologies. I love learning, and I think I'm a very fast learner.

I have a BSc degree in Discrete math and Computer science from Reykjavík University where I graduated top of my class (grade average 9.8 out of 10).

Contact me if you're interested in working together!

Location: Iceland

Remote: Yes | Hybrid | In-Office

Willing to relocate: Yes

Technologies: Python, C++, PyTorch, Deep Learning, Computer Vision, Concurrent Programming, PostgreSQL.

Email: [email protected]

CV: https://github.com/Robertleoj/resume/raw/main/resume.pdf

I'm an ML engineer early in my career. I'm looking for a position in the US or EU at a company with interesting tech, and a meaningful mission that accelerates human progress.

I specialize in computer vision. I've been working on a computer vision for real-time analysis of golf swings, with calibrated cameras in a golf simulator.

In my previous job I made made models for multiple business use cases, such as demand forecasting.

I've only worked in small companies where, in addition to the AI, I've had a wide range of responsibilities, so I have experience with many technologies. I love learning, and I think I'm a very fast learner.

I have a BSc degree in Discrete math and Computer science from Reykjavík University where I graduated top of my class (grade average 9.8 out of 10).

Contact me if you're interested in working together!

Location: Iceland

Remote: Yes | Hybrid | In-Office

Willing to relocate: Yes

Technologies: Python, C++, PyTorch, Deep Learning, Computer Vision, Concurrent Programming, PostgreSQL.

Email: [email protected]