Data-driven correction for the masking model of Smith
Bibliographic record
Abstract
To render realistic material appearances, physically based models often rely on the microfacet theory. These models require several parameters that drive the distribution of microfacet orientations, their reflectance, and a geometric attenuation factor. The latter accounts for self-masking and self-shadowing; it must be managed carefully when physical plausibility is required. The masking term proposed by Smith [IEEE Trans. Antennas Propag.15, 668 (1967)IETPAK0018-926X10.1109/TAP.1967.1138991] is widely used for its accuracy when employed with theoretical distributions. However, it does not ensure exactness when compared with the masking of measured microsurfaces. We have conducted an in-depth study of the error associated with isotropic roughnesses, based on a ray-casting measurement with mesh-based surfaces. This article proposes a correction function that can be added to the theoretical masking term at a very low computation cost while bringing the masking closer to the ground truth. Our correction term is built from a linear combination of two Johnson SB distributions, parameterized according to statistical features of the microsurface. We show that the resulting masking term always reduces the error when compared to the original Smith term alone. This improvement is illustrated in the whole bidirectional reflectance functions with rendered images.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".