Bibliographic record
Abstract
We present a low-cost 3D tracking system for virtual reality, gesture modeling, and robot manipulation applications which require fast and precise localization of headsets, data gloves, props, or controllers. Our system removes the need for cameras or projectors for sensing, and instead uses cheap LEDs and printed masks for illumination, and low-cost photosensitive markers. The illumination device transmits a spatiotemporal pattern as a series of binary Gray-code patterns. Multiple illumination devices can be combined to localize each marker in 3D at high speed (333Hz). Our method has strengths in accuracy, speed, cost, ambient performance, large working space (1m-5m) and robustness to noise compared with conventional techniques. We compare with a state-of-the-art instrumented glove and vision-based systems to demonstrate the accuracy, scalability, and robustness of our approach. We propose a fast and accurate method for hand gesture modeling using an inverse kinematics approach with the six photosensitive markers. We additionally propose a passive markers system and demonstrate various interaction scenarios as practical applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.374 | 0.224 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".