Cycle shading for the assessment and visualization of shape in one and two codimensions
Bibliographic record
Abstract
In this paper we propose cycle shading and hatched cycle shading as new local shading techniques for shape assessment and visualization. Natural surface highlights are extended to not only appear in isolated parts of a surface, but to reappear throughout the surface in a regular and easy-to-control pattern. Thereby even small surface variations become visible, wherever they are located on the surface. We further extend (hatched) cycle shading to curves in 3D, i.e., to shapes of higher codimension. We demonstrate how (hatched) cycle shading improves 3D vector field visualization by showing higher-order discontinuities of streamlines, pathlines, or streaklines. Our visualization approach is generic, simple, efficient, and can readily be used where Phong illumination is applicable because information on curvature or mesh connectivity is not required. The effectiveness of cycle shading for the assessment of surface quality is demonstrated by a user study. Finally, this paper addresses issues of anti-aliasing, parameter control, applications, and efficient GPU implementations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".