A computational theory of da Vinci stereopsis
Bibliographic record
Abstract
In binocular vision, occlusion of one object by another gives rise to monocular occlusions—regions visible only in one eye. Although binocular disparities cannot be computed for these regions, monocular occlusions can be precisely localized in depth and can induce the perception of illusory occluding surfaces. The phenomenon of depth perception from monocular occlusions, known as da Vinci stereopsis, is intriguing, but its mechanisms are not well understood. We first propose a theory of the mechanisms underlying da Vinci stereopsis that is based on the psychophysical and computational literature on monocular occlusions. It postulates, among other principles, that monocular areas are detected explicitly, and depth from occlusions is calculated based on constraints imposed by occlusion geometry. Next, we describe a biologically inspired computational model based on this theory that successfully reconstructs depth in a large range of stimuli and produces results similar to those described in the psychophysical literature. These results demonstrate that the proposed neural architecture could underpin da Vinci stereopsis and other stereoscopic percepts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".