Une parametrisation non linéaire mais versatile du manifolde des BRDFs
Bibliographic record
Abstract
Real-world reflectance data can be used to improve the realism of synthesized images,albeit with many challenges: memory footprints can be large, profiles are limited to a finite (usuallysmall) set of materials and rendering with measured data can be costly. Since the observationspace (number of reflectance measurements) is usually much larger than the underlying space ofreal-world reflectance profiles, a typical optimisation strategy identifies principal components inthe data to directly render from compressed representations of the measurements. We directlylearn an underlying low-dimensional non-linear reflectance manifold amenable to rapid explorationand rendering of the space of real-world materials. We show that interpolated materials can beexpressed as linear combinations of the measured data, despite lying on a non-linear manifold.This allows us to efficiently interpolate, extrapolate and render directly from the manifold. Weapply a Gaussian process latent variable model to represent the reflectance manifold, demonstratingits utility in the context of high-performance and realistic rendering with materials that areinterpolations of acquired BRDFs (from the popular MERL dataset).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".