How does lighting direction affect shape perception of glossy and matte surfaces?
Bibliographic record
Abstract
To visualize the shape of 2D surface data, one often renders it using a simple model with matte or glossy reflectance and a light source at infinity. The parameter choices for this model are typically ad hoc, however, and previous studies have provided varying evidence on what this choice should be so that the shape is perceived as accurately as possible. Here we present an experiment that examines local qualitative shape perception on matte and glossy surfaces where we vary both the overall slant of the surface with respect to the viewer and the slant of the distant light source. We find that increasing the slant of the light source to twice that of the surface slant angle improves subjects' perception of qualitative shape of glossy surfaces. Additionally, at these high slant angles the glossy surface percepts are better than those of matte surfaces. We argue that these improvements are due to the positioning of the highlights at the peaks and valleys of the terrain, where they demarcate the surface maxima. We also find that increasing the light slant produces better and/or more consistent shape percepts than the default lighting in commercial visualization software such as Matlab and Mathematica.
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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 | 0.005 |
| 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 | 0.000 |
| 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".