Neural Histogram‐Based Glint Rendering of Surfaces With Spatially Varying Roughness
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The complex, glinty appearance of detailed normal‐mapped surfaces at different scales requires expensive per‐pixel Normal Distribution Function computations. Moreover, large light sources further compound this integration and increase the noise in the Monte Carlo renderer. Specialized rendering techniques that explicitly express the underlying normal distribution have been developed to improve performance for glinty surfaces controlled by a fixed material roughness. We present a new method that supports spatially varying roughness based on a neural histogram that computes per‐pixel NDFs with arbitrary positions and sizes. Our representation is both memory and compute efficient. Additionally, we fully integrate direct illumination for all light directions in constant time. Our approach decouples roughness and normal distribution, allowing the live editing of the spatially varying roughness of complex normal‐mapped objects. We demonstrate that our approach improves on previous work by achieving smaller footprints while offering GPU‐friendly computation and compact representation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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