Analysis of Depth Perception with Virtual Mask in Stereoscopic AR
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
A practical application of Augmented Reality (AR) is see-through vision, a technique that enables a user to observe an inner object located behind a real object by superimposing the virtually visualized inner object onto the real object surface (for example, pipes and cables behind a wall or under a floor). A challenge in such applications is to provide proper depth perception when an inner virtual object image is overlaid on a real object. To improve depth perception in stereoscopic AR, we propose a method that overlays a random-dot mask on the real object surface. This method conveys to the observers the illusion of observing the virtual object through many small holes. We named this perception ''stereoscopic pseudo-transparency.'' Our experiments investigated (1) the effectiveness of the proposed method in improving the depth perception between the real object surface and the virtual object compared to existing methods, and (2) whether the proposed method can be used in an actual AR environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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