On the distinction between perceived & predicted depth in S3D films
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.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".