Calculating surface reflectance using a single-bounce model of mutual reflection
Bibliographic record
Abstract
Light reflected from one surface onto a second surface changes both the intensity and spectral power distribution of light leaving the second surface. Similarly, light from the second surface illuminates the first. This mutual reflection effect can be exploited by examining pixels where interreflection is and is not present. From these measurements several intrinsic properties can be determined: the reflectance of each surface, the spectral power distribution of the incident illumination, and some constraints on the physical configuration of the two surfaces. The authors use finite dimensional linear models for the ambient illumination and for surface spectral reflectance, with m basis functions for illumination and n for surfaces. Examining p sensor values (e.g. RGB values) they find that if p satisfies the condition p>or=(2n+m)/3 they can solve for finite dimensional model descriptors of both surfaces and of the ambient illumination, as well as for a form-factor stemming from the surface configuration. With n=m=3, p can also be 3. A single-bounce model of mutual reflection accounts for the most important contribution to light intensity in an interreflecting geometry.>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
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".