Bibliographic record
Abstract
It is known that most real surfaces are neither perfectly diffuse (Lambertian) nor ideally specular (mirror-like); however, most shape-from-shading algorithms assume Lambertian reflectance. It is necessary to develop new techniques to solve the shape-from-shading problem. These techniques must be able to recover the shape of objects whose surfaces are not necessarily Lambertian. A new heuristic-based algorithm, called the general shading logic algorithm, is proposed to recover the shape of objects whose surfaces are non-Lambertian. This algorithm is based on the shading logic algorithm recently proposed by Vega and Yang (see IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, no.6, p.592-597, 1993). The proposed algorithm is flexible enough to work with a more general reflectance model. To demonstrate that the proposed algorithm can cope with a wide range of reflectance models, a physically-based model for light reflection is used that can approximate rough surfaces. The model of light reflection used is similar to the Torrance-Sparrow (1967) approach. The general shading logic algorithm has been implemented and evaluated experimentally. The experimental results of the proposed algorithm are very encouraging and the performance is demonstrated by extensive experiments using a wide variety of synthesized and real objects.>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".