Improving the Reliability/Cost Ratio of Goniophotometric Comparisons
Bibliographic record
Abstract
Many scattering models have been presented in the graphics literature. Few of them, however, have been evaluated through comparisons with real measured data. As the demand for plausible and predictable scattering models increases, more attention is given to performing such comparisons. In this paper, we examine the implementation of virtual goniphotometers used to obtain BRDF (Bidirectional Reflect ance Distribution Function) and BTDF (Bidirectional Transmittance Distribution Function) records from algorithmic scattering models. These records can be compared to data from actual experiments in order to validate the models. Our discussion focuses on practical issues, namely the subdivision of the devices' collector sphere and the ray density required to obtain reliable BRD F and BTDF estimates. The subdivision techniques examined in this paper have been used before in publications, but the details of their computation are not readily available in the literature. Although the mathematical bound presented to determine appropriate ray densities for virtua l goniphotometers is a direct generalization of a bound used for virtual spectrophotometers, it has not been published be fore. Our discussion of these issues is supported by practical experiments whose results are also provided in this paper.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".