Neural network to reconstruct specular surface shape from its three shading images
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Bibliographic record
Abstract
This paper proposes a new method to reconstruct the shape of the specular surface by learning the mapping between three image irradiances observed under the illumination from three lighting directions and the corresponding surface gradient. The method uses Phong reflectance function (1975) which can describe the specular reflectance including Lambertian reflectance, and its neural network is constructed to determine the values of reflectance parameters and the objective surface gradient distribution under the condition that the values of reflectance parameters included in this function are unknown. The method reconstruct the surface gradient distribution after determining the values of reflectance parameters of a test object using two step neural network which consist of one to extract two gradient parameters from three image irradiances and its opposite one. The effectiveness of this proposed neural network was confirmed by computer simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it