Motion in depth constancy in stereoscopic displays
Bibliographic record
Abstract
In a stereoscopic 3D scene, non-linear mapping between real space and disparity could produce distortions when camera geometry differs from natural stereoscopic geometry. When the viewing distance and zero screen parallax setting are held constant and interaxial separation is varied, there is an asymmetric distortion in the mapping of stereoscopic to real space. If an object traverses this space at constant velocity, one might anticipate distortion of the perceived velocity. To determine if the predicted distortions are in fact perceived, we assessed perceived acceleration and deceleration using an animation of a ball moving in depth through a simulated environment, viewed stereoscopically. The method of limits was used to measure transition points between perceived acceleration and deceleration as a function of interaxial and context (textured vs. non-textured background). Based on binocular geometry, we predicted that the transition points would shift toward deceleration for small and towards acceleration for large interaxial separations. However, the average transition values were not influenced by interaxial separation. These data suggest that observers are able to discount distortions of stereoscopic space in interpreting the object motion. These results have important implications for the rendering or capture of effective stereoscopic 3D 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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".