Depth percepts from monocular self-occlusions in 3D objects
Bibliographic record
Abstract
To date, the impact of monocular half-occlusions on depth perception has been studied almost exclusively in the context of foreground/background occlusion where, when viewing a stimulus binocularly, a surface occludes part of the background in one eye. However, monocular regions also arise from self-occlusion where an object occludes regions within itself. Previous research has shown that in two-surface arrangements the size and texture of the monocular region impacts the perceived depth between the occluder and the occluded region. In the case of self-occlusions, misinterpretation of monocular regions could result in distortions in the perceived 3D form of an object. Here we evaluate depth percepts in the presence of monocular self-occlusions for 3D objects. Specifically, we assess the impact of i) texture gradients within the occluded region and ii) object shape from binocular disparity, on the perceived extent of the object in depth. Stimuli were textured half-cylinders rendered with perspective projection and viewed on a mirror stereoscope. Perceived depth was assessed using a magnitude estimation task. Our results show that inconsistent monocular texture gradient information in self-occlusions does not influence depth estimates when familiar object shape from disparity is present. However, when observers are unable to use binocular disparity to extrapolate 3D shape, they do rely on 2D texture cues to make depth estimates. Under these conditions, when monocular texture is inconsistent with the binocular texture, depth is significantly underestimated. We conclude that, unlike two-surface occlusions, the visual system weighs depth information from self-occlusions depending on the availability of additional information about 3D object shape.
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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) | 0.000 | 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) | 0.002 | 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".