Crosstalk reduces the amount of depth seen in 3D images of natural scenes
Bibliographic record
Abstract
Crosstalk remains an important determinant of stereoscopic 3D (S3D) image quality. Defined as the leakage of one eye's image into the image of the other eye it affects all commercially available stereoscopic viewing systems. Previously we have shown that crosstalk affects perceived depth magnitude in S3D displays. We found that perceived depth between two lines separated in depth decreased as crosstalk increased. The experiments described here extend our previous work to complex images of natural scenes. We controlled crosstalk levels by simulating them in images presented on a zero-crosstalk mirror stereoscope display. The observers were asked to estimate the amount of stereoscopic depth between pairs of objects in stereo-photographs of cluttered rooms. Data show that as crosstalk increased perceived depth decreased; an effect found at all disparities. Similarly to our previous experiments a significant decrease in perceived depth was observed with as little as 2-4% crosstalk. Taken together these results demonstrate that our previous findings generalize to natural scenes and show that crosstalk reduces perceived depth magnitude even in natural scenes with pictorial depth cues.
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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 | 0.001 |
| 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) | 0.003 | 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".