u/L42ARO

So I've always argued that Physical AI for robotics need actionable outputs like 3D coordinates, not bullet points or nice paragraphs.
So decided to experiment by combining a VLM with Monocular Depth Estimation, essentially projecting 2D reasoning into 3D, I called it Odyseus - Spatial VLM

Tech Stack:
- VLM: Qwen 3.6
- Depth Estimation: Depth Anything 3 - Metric Large

Worked pretty well, figured to share, check repo: https://github.com/MercuriusTech/Odyseus-Spatial-VLM

So I've always argued that Physical AI for robotics need actionable outputs like 3D coordinates, not bullet points or nice paragraphs.
So decided to experiment by combining a VLM with Monocular Depth Estimation, essentially projecting 2D reasoning into 3D, I called it Odyseus - Spatial VLM

Tech Stack:
- VLM: Qwen 3.6
- Depth Estimation: Depth Anything 3 - Metric Large

Worked pretty well, figured to share, check repo: https://github.com/MercuriusTech/Odyseus-Spatial-VLM

Hi, so I am having a hard time getting a low distortion camera (around 60deg-90deg FOV), so I was forced to use a wide 160deg fisheye camera. I need it for a vSLAM platform I'm building, but the raw video itself was too distorted for it to be good, so I vibecoded a toolkit to figure out the intrinsic parameters of my camera and be able to undistort the footage. It took me some time, at first the distortion was still there, so I went ahead and created a program that helped me sample ~60 frames with a mini guide on which positions I should record for best results, and yeah it worked, I was able to undistort my video from my 160deg camera, so I figured to share.

I know this ain't nothing new or ground breaking, there are probably tools out there that already do this and I was just too lazy to look them up and set them up, but hey if this turns out helpful for someone besides just me, I'm happy with that.

REPO LINK: https://github.com/L42ARO/Fisheye-Calibration