One-shot color mapping of a ray direction field for obtaining three-dimensional profiles integrating deep neural networks
Bibliographic record
Abstract
A method for simultaneously and instantly obtaining both a three-dimensional (3D) surface and its inclination angle distribution from a single image captured by an imaging system equipped with a coaxial multicolor filter that integrates deep neural networks (DNNs) is proposed. The imaging system can obtain a light-ray direction in the field of view through one-shot color mapping. Light rays reflected from a 3D surface, even if it has microscale height variations with a small inclination angle distribution, can be assigned different colors depending on their directions by the imaging system. This enables the acquisition of the surface inclination angle distribution. Assuming a smooth and continuous 3D surface, it is possible to reconstruct the surface from a single captured image using DNNs. The DNNs can provide the height variations of the 3D surface by solving a nonlinear partial differential equation that represents the relationship between height variation and the direction of light rays. This method is validated analytically and experimentally using microscale convex surfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".