A Real Time Augmented Reality System Using GPU Acceleration
Bibliographic record
Abstract
Augmented Reality (AR) is an application of computer vision that is processor intensive and typically suffers from a trade-off between robust view alignment and real time performance. Real time AR that can function robustly in variable environments is a process difficult to achieve on a PC (personal computer) let alone on the mobile devices that will likely be where AR is adopted as a consumer application. Despite the availability of high quality feature matching algorithms such as SIFT, SURF and robust pose estimation algorithms such as EPNP, practical AR systems today rely on older methods such as Harris/KLT corners and template matching for performance reasons. SIFT-like algorithms are typically used only to initialize tracking by these methods. We demonstrate a practical system with real ime performance using only SURF without the need for tracking. We achieve this with extensive use of the Graphics Processing Unit (GPU) now prevalent in PC's. Due to mobile devices becoming equipped with GPU's we believe that this architecture will lead to practical robust AR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".