Perceptual Tolerance to Stereoscopic 3D Image Distortion
Bibliographic record
Abstract
An intriguing aspect of picture perception is the viewer’s tolerance to variation in viewing position, perspective, and display size. These factors are also present in stereoscopic media, where there are additional parameters associated with the camera arrangement (e.g., separation, orientation). The predicted amount of depth from disparity can be obtained trigonometrically; however, perceived depth in complex scenes often differs from geometric predictions based on binocular disparity alone. To evaluate the extent and the cause of deviations from geometric predictions of depth from disparity in naturalistic scenes, we recorded stereoscopic footage of an indoor scene with a range of camera separations (camera interaxial (IA) ranged from 3 to 95 mm) and displayed them on a range of screen sizes. In a series of experiments participants estimated 3D distances in the scene relative to a reference scene, compared depth between shots with different parameters, or reproduced the depth between pairs of objects in the scene using reaching or blind walking. The effects of IA and screen size were consistently and markedly smaller than predicted from the binocular viewing geometry, suggesting that observers are able to compensate for the predicted distortions. We conclude that the presence of multiple realistic monocular depth cues drives normalization of perceived depth from binocular disparity. It is not clear to what extent these differences are due to cognitive as opposed to perceptual factors. However, it is notable that these normalization processes are not task specific; they are evident in both perception- and action-oriented tasks.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".