A directionally adaptive edge anti-aliasing filter
Bibliographic record
Abstract
Figure 1: Steps of the Directionally Adaptive Edge Anti-Aliasing Filter algorithm. The left frame shows geometry edge pixels in the scene determined from the hardware MSAA samples. The center frame represents the gradients at the pixels to be filtered. The right frame is the final image where filtered colors for the pixels in the center image are derived using MSAA samples from a neighborhood of 3x3 pixels and 72 subsample values. (Images generated from Futuremark 3DMark03.) The latest generation of graphics hardware provides direct access to multisample anti-aliasing (MSAA) rendering data. By taking advantage of these existing pixel subsample values, an intelligent reconstruction filter can be computed using programmable GPU shader units. This paper describes an adaptive anti-aliasing (AA) filter for real-time rendering on the GPU. Improved quality is achieved by using information from neighboring pixel samples to compute both an approximation of the gradient of primitive edges and the final pixel color.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".