Stereoscopic distortions when viewing geometry does not match inter-pupillary distance
Bibliographic record
Abstract
The relationship between depth and binocular cues (disparity and convergence) is defined by the distance separating the two eyes, also known as the inter-pupillary distance (IPD). This relationship is mapped in the visual system through experience and feedback, and adaptively recalibrated as IPD gradually increases during development. However, with the advent of stereoscopic-3D displays, situations may arise in which the visual system views content that is captured or rendered with a camera separation that differs from the viewer’s own IPD; without feedback, this will likely result in a systematic and persistent misperception of depth. We tested this prediction using a VR headset in which the inter-axial separation of virtual cameras and the separation between the optics are coupled. Observers (n=15) were asked to adjust the angle between two intersecting textured-surfaces until it appeared to be 90°, at each of three viewing distances. In the baseline condition the lens and camera separations matched each observer’s IPD. In two ‘mismatch’ conditions (tested in separate blocks) the lens and camera separations were set to the maximum (71 mm) and minimum (59 mm) allowed by the headset. We found that when the lens and camera separation were less than the viewer’s IPD they exhibited compression of space; the adjusted angle was smaller than their baseline setting. The reverse pattern was seen when the lens and camera separation were larger than the viewer’s IPD. Linear regression analysis supported these conclusions with a significant correlation between the magnitude of IPD mismatch and the deviation of angle adjustment relative to the baseline condition. We show that these results are well explained by a geometric model that considers the scaling of disparity and convergence due to shifts in virtual camera and optical inter-axial separations relative to an observer’s IPD.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".