Specularity and Shadow Interpolation via Robust Polynomial Texture Maps
Bibliographic record
Abstract
Polynomial Texture Maps (PTM)[SIGGRAPH 2001] form an alternative method for apprehending surface colour and albedo that extends a simple model of image formation from the Lambertian variant of Photometric Stereo (PST) to more general reflectances. Here we consider solving such a model in a robust version, not to date attempted for PTM. But the main upshot of utilizing robust regression is in the identification of both shadows and specularities automatically, without the need for any thresholds, in a tripar-tite set of weights for pixels that are labelled as matte, shadow, or specularity. Original images are captured using a hemispherical set of lights, and pixel values across the light-ing directions are then labelled as inliers, or outliers of two types. A per-pixel robust regression on luminance is carried out using Least Median of Squares, and automatically-identified outlier pixels are labelled as shadows if they are darker than matte and corre-spondingly, specular outliers are too bright. Inlier identification generates correct values for chromaticity and for surface albedo and thus matte luminance and colour. Then a robust version of PST, using only PTM inliers, improves estimates of normal vectors and albedo recovered. With specular pixel values over the lights in hand we model specu-larity using a radial basis function (RBF) regression, and non-specular pixel departures from matte using a second RBF set. Then for a new lighting direction, we can readily interpolate both specular content as well as shadows. 1
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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) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".