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Open modular tracking stack for VR/MR headsets (eye, SLAM, FBT, BCI) [video]

Open modular tracking stack for VR/MR headsets (eye, SLAM, FBT, BCI) [video]

by WalkerDev·May 15, 2026·2 points·0 comments
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AI Analysis

●●SolidBold BetNiche Gem

Rare open-source alternative to proprietary eye-tracking stacks like Tobii or Pupil Labs.

Strengths
  • •Modular architecture allows swapping tracking components like SLAM and BCI independently.
  • •Privacy-first edge processing keeps biometric data off the cloud entirely.
Weaknesses
  • •Codebase isn't fully modular yet, currently just a webcam demo pipeline.
  • •Accuracy metrics (80-85%) lag significantly behind commercial hardware standards.
Category
Hardware
Target Audience

VR/MR hardware developers and open-source headset builders

Similar To

Pupil Labs · Tobii Pro · OpenXR

Post Description

My team and I have been building a VR headset + MR glasses platform for the past few years, and one of the biggest challenges has been tracking.

A few months ago we started developing a modular tracking stack called Shinra Meisin, focused on:

- Low-latency edge processing - Privacy-first architecture - Modularity and hardware flexibility - XR-focused tracking pipelines

The system currently includes:

- Eye tracking - Mouth tracking - SLAM - Inside-out full body tracking - Experimental BCI integration

Today we’re showing our current eye tracking progress and plan on showing off SLAM within the next two(ish) days.

Real time landmark accuracy is 80-85% and gaze is 1.0-2.0° (Which will increase exponentially once I get John (My dev) a better GPU and help train a dataset off of 40ish people

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