Identifying discontinuities in depth: A role for monocular occlusions
Bibliographic record
Abstract
It is well established that monocular regions arising from occlusion of one object by another contribute to stereoscopic depth perception. However, the exact role of monocular occlusions in 3D scene perception remains unclear. One possibility is that monocular occlusions define object boundaries or discontinuities in depth. This is an attractive possibility, but to date it has not been tested empirically. Here we describe a series of experiments that directly test this hypothesis. Our novel stereoscopic stimulus consists of a foreground rectangular region set against a random-dot background positioned at zero disparity. One side of the foreground region is filled with a random-dot texture shifted towards the observer in apparent depth. The remaining area of the foreground is blank and carries no disparity information. In several experiments, we vary the presence or absence and the width of occluded areas at the border of the central blank area and the background texture. Our data show that the presence of occluded elements on the boundary of the blank area dramatically influences the perceived shape of the foreground region. If there are no occluded elements, the foreground appears to contain a depth step, as the blank area lies at the depth of the zero disparity border. When occluded elements are added, the blank region is seen vividly at the same depth as the texture, so that the foreground is perceived as a single opaque planar surface. We show that the depth perceived via occlusion is not due to the presence of binocular disparity at the boundary, and that it is qualitative, not quantitative in nature. Taken together, our experiments provide strong support for the hypothesis that monocular occlusion zones are important signals for the presence and location of depth discontinuities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".