A Non-Parametric Factor Microfacet Model for Isotropic BRDFs
Bibliographic record
Abstract
We investigate the expressiveness of the microfacet model for isotropic bidirectional reflectance distribution functions (BRDFs) measured from real materials by introducing a non-parametric factor model that represents the model’s functional structure but abandons restricted parametric formulations of its factors. We propose a new objective based on compressive weighting that controls rendering error in high-dynamic-range BRDF fits better than previous factorization approaches. We develop a simple numerical procedure to minimize this objective and handle dependencies that arise between microfacet factors. Our method faithfully captures a more comprehensive set of materials than previous state-of-the-art parametric approaches yet remains compact (3.2KB per BRDF). We experimentally validate the benefit of the microfacet model over a naïve orthogonal factorization and show that fidelity for diffuse materials is modestly improved by fitting an unrestricted shadowing/masking factor. We also compare against a recent data-driven factorization approach [Bilgili et al. 2011] and show that our microfacet-based representation improves rendering accuracy for most materials while reducing storage by more than 10 ×.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".