Color photometric stereo and virtual image rendering using neural networks
Bibliographic record
Abstract
Abstract In this paper we extend the application of neural network‐based photometric stereo founded on the principle of empirical photometric stereo to color images proposing a method for computing both the normal vectors of a target object and its color reflectance coefficients. This method is able to render objects that have non‐Lambert reflectance properties without using any parametric reflectance function as a reflectance model. In addition, we propose a novel neural network‐based rendering method that allows the generation of realistic virtual images of an object with arbitrary light source direction and from arbitrary viewpoints based on the physical reflectance properties of the actual object and perform a comparative evaluation with approximations by existing models, the Phong model, and the Torrance–Sparrow model. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(12): 47–60, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecjb.20423
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".