A markerless augmented reality system for mobile devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Augmented Reality (AR) combines a live camera view of a real world environment with computer-generated virtual content. Alignment of these viewpoints is done by recognizing artificial fiducial markers, or, more recently, natural features already present in the environment. This is known as Marker-based and Markerless AR respectively. We present a markerless AR system that is not limited to artificial markers, but is capable of rendering augmentations over user-selected textured surfaces, or ‘maps’. The system stores and differentiates between multiple maps, all created online. Once recognized, maps are tracked using a hybrid algorithm based on feature matching and inlier tracking. With the increasing ubiquity and capability of mobile devices, we believe it is possible to perform robust, markerless AR on current generation tablets and smartphones. The proposed system is shown to operate in real-time on mobile devices, and generate robust augmentations under a wide range of map compositions and viewing conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it