Simulated bidirectional texture functions with silhouette details
Bibliographic record
Abstract
The representation of material appearance requires an understanding of the underlying structures of real surfaces, light-material interaction, and human visual system. The Bidirectional Texture Function (BTF) describes real-world materials as a spatial variation of reflectance, which depends on view and light directions. Real BTFs integrate all optical phenomena occurring in a complex material, such as self-occlusions, interreflections, subsurface scattering, etc., independently of the mesoscopic surface geometry. In this paper, we revisit BTF simulation to improve the modeling of surface appearance. In the recent years, computer graphics has achieved very good levels of image realism on geometrical appearance of 3D scenes. It is therefore logical to think that using this technology to simulate visual effects at the level of the mesoscopic geometry should provide even more realistic simulated BTFs. Our ultimate goal here is thus to produce material appearance as rich and as similar as those in reality, but relying more on the intuition and skills of artists, and on the rendering capacity of today’s computer graphics. We have designed a virtual parallel-projection / directional incident illumination framework that exploits rendering coherency in order to produce, in reasonable rendering times and with good compression ratios, BTFs of complex mesoscopic geometry, and this, even at grazing angles. Our current framework can simulate efficiently local interreflections effects within mesoscopic structures, as well as effects due to transparency, silhouettes, and surface curvatures. Our general simulation framework should also prove extensible to several other visual phenomena. Index Terms: BTF, BRDF, aBRDF, simulation, surface appearance, compression, mesoscopic geometry, silhouette.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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 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".