<title>The camera convergence problem revisited</title>
Bibliographic record
Abstract
Convergence of the real or virtual stereoscopic cameras is an important operation in stereoscopic display systems. For example, convergence can shift the range of portrayed depth to improve visual comfort; can adjust the disparity of targets to bring them nearer to the screen and reduce accommodation-vergence conflict; or can bring objects of interest into the binocular field-of-view. Although camera convergence is acknowledged as a useful function, there has been considerable debate over the transformation required. It is well known that rotational camera convergence or 'toe-in' distorts the images in the two cameras producing patterns of horizontal and vertical disparities that can cause problems with fusion of the stereoscopic imagery. Behaviorally, similar retinal vertical disparity patterns are known to correlate with viewing distance and strongly affect perception of stereoscopic shape and depth. There has been little analysis of the implications of recent findings on vertical disparity processing for the design of stereoscopic camera and display systems. We ask how such distortions caused by camera convergence affect the ability to fuse and perceive stereoscopic images.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".