Qualitative shape from shading, highlights, and mirror reflections
Bibliographic record
Abstract
The human visual system has a remarkable ability to perceive three-dimensional (3-D) surface shape from shading and specular reflections. This paper presents two experiments that examined the perception of local qualitative shape under various conditions. Surfaces were rendered using standard computer graphics models of matte, glossy, and mirror reflectance and were viewed from a small oblique angle to avoid occluding contour shape cues. The subjects' task was to judge whether a marked point on each surface lay on a local hill or valley. In the first experiment, performance was lower for glossy surfaces than matte surfaces, which is contrary to findings in previous studies of quantitative shape. For mirror surfaces, performance was high despite the absence of occluding contours, and performance was increased when the environment map was brighter in the upper hemisphere as in a natural environment. The second experiment examined how subjects resolve a depth-reversal shape ambiguity where surfaces can be either upward or downward facing. An upward-facing surface prior that is known to exist for matte surfaces was also found to exist for glossy and mirror surfaces. Subjects relied entirely on this prior to resolve the depth-reversal ambiguity for matte and glossy surfaces, but relied on perspective cues as well for mirror surfaces.
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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) | 0.001 | 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".