Filtering distributions of normals for shading antialiasing
Why this work is in the frame
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Bibliographic record
Abstract
High-frequency illumination effects, such as highly glossy highlights on curved surfaces, are challenging to render in a stable manner. Such features can be much smaller than the area of a pixel and carry a high amount of energy due to high reflectance. These highlights are challenging to render in both offline rendering, where they require many samples and an outliers filter, and in real-time graphics, where they cause a significant amount of aliasing given the small budget of shading samples per pixel. In this paper, we propose a method for filtering the main source of highly glossy highlights in microfacet materials: the Normal Distribution Function (NDF). We provide a practical solution applicable for real-time rendering by employing recent advances in light transport for estimating the filtering region from various effects (such as pixel footprint) directly in the parallel-plane half-vector domain (also known as the slope domain), followed by filtering the NDF over this region. Our real-time method is GPU-friendly, temporally stable, and compatible with deferred shading, normal maps, as well as with filtering methods for normal maps.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it