Qualitative shape from shading, specular highlights, and mirror reflections
Bibliographic record
Abstract
Surface appearance cues such as shading and specularities provide a rich source of information about 3D shape. Here we present an experiment that compared qualitative shape perception from such cues. We used OpenGL to render smooth bumpy terrain surfaces under perspective projection and under several rendering models, including the Phong model with a point light source and a mirror reflection model with a complex orientation-free environment. Each rendered terrain surface had a floor slant of 30 degrees consistent with a view-from-above prior, and each was presented either statically or rotating. The task was to judge whether a marked point on each surface was on a hill or in a valley. Percent correct scores (12 observers) were highest in the 'Lambertian only' condition, lower in the 'mirror only' and 'broad highlight only' conditions, and lowest in the 'narrow highlight only' condition. No effect was found for rotating vs. static presentation. The results are somewhat inconsistent with (Norman et al, Psych. Sci. 2004) who found that highlights improve performance in a shape discrimination task. The results are more consistent with (Fleming et al, J. Vision, 2004) who found that specularities can provide shape information if they produce a dense set of image gradients that covary with the second derivatives of depth. This requirement was met by all the rendering models above, except for 'narrow highlights only'. Meeting abstract presented at VSS 2012
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".