On seeing transparent surfaces in stereoscopic displays
Bibliographic record
Abstract
Transparency presents an extreme challenge to stereoscopic correspondence and surface interpolation, particularly in the case of multiple transparent surfaces in the same visual direction. In this experiment we manipulate density, separation in depth, and number of transparent planes within a single experimental design, to evaluate the constraints on stereoscopic transparency. We use a novel task involving identification of patterned planes among the planes constituting the stimulus. The results show that, under these conditions, (1) subjects are able to perceive up to five transparent surfaces concurrently; (2) the transparency percept is impaired by increasing the texture density; (3) the transparency percept is initially enhanced by increasing the disparity between the surfaces; (4) the percept begins to degrade as disparity between surfaces is further increased beyond an optimal disparity, which is a function of element density. Specifically, at higher texture densities the optimal disparity shifts to smaller values. This interaction between disparity and texture density is surprising, but it can account for discrepancies in the existing literature. We are currently testing extended correlational and feature-based models of stereopsis with our stimuli. This will provide insight into our psychophysical results and a basis for quantitative evaluation of existing computational models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".