TangiAR: Markerless Tangible Input for Immersive Augmented Reality with Everyday Objects
Bibliographic record
Abstract
Tangible interactions with everyday objects have been shown to be fast, accurate, and natural, and have shown promise when combined with immersive augmented reality. However, implementing tangible controls presents considerable challenges. Previous works in the field either rely on additional tracking markers on objects, inadvertently shifting the difficulty to users, or are too computationally demanding for real-time operation on a head-mounted display (HMD). We propose TangiAR, a tangible control system which tracks everyday objects without the need for fiducial trackers, enabling them as passive controllers and virtual proxies in AR applications. TangiAR additionally enables hand and finger proximity interactions with tangibles, further expanding the interaction space. TangiAR can run on an unmodified Microsoft HoloLens 2, making it immediately practical. We evaluated the performance of TangiAR through a technical evaluation, including occlusion robustness and tracking accuracy tests, and a user study which examined the usability of our markerless object tracking system in various AR interactions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".