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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".