On the distinction between perceived & predicted depth in S3D films
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 primary concern when making stereoscopic 3D (S3D) movies is to promote an effective and comfortable S3D experience for the audience when displayed on the screen. The amount of depth produced on-screen can be controlled using a variety of parameters. Many of these are lighting related such as lighting architecture and technology. Others are optical or positional and thus have a geometrical effect including camera interaxial distance, camera convergence, lens properties, viewing distance and angle, screen/projector properties and viewer anatomy (interocular distance). The amount of estimated depth from disparity alone can be precisely predicted from simple trigonometry; however, perceived depth from disparity in complex scenes is difficult to evaluate and most likely different from the predicted depth based on geometry. This discrepancy is mediated by perceptual and cognitive factors, including resolution of the combination/conflict of pictorial, motion and binocular depth cues. This paper will review geometric predictions of depth from disparity and present the results of experiments which assess perceived S3D depth and the effect of the complexity of scene content.
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 | 0.000 | 0.000 |
| 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