A Predictive Light Transport Model for the Human Iris
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Recently, light interactions with organic matter have become the object of detailed investigations by image synthesis researchers. Besides allowing these materials to be rendered in a more intuitive manner, these efforts aim to extend the scope of computer graphics applications to areas such as applied optics and biomedical imaging. There are, however, organic materials that still lack predictive simulation solutions. Among these, the ocular tissues, especially those forming the human iris, pose the most challenging modeling problems which are often associated with data scarcity. In this paper, we describe the first biophysically‐based light transport model for the human iris ever presented in the scientific literature. The proposed model algorithmically simulates the light scattering and absorption processes occurring within the iridal tissues, and computes the spectral radiometric responses of these tissues. Its design is based on the current scientific understanding of the iridal morphological and optical characteristics, and it is controlled by parameters directly related to these biophysical attributes. The accuracy and predictability of the spectral results provided by the model are evaluated through comparisons with actual measured iridal data, and its integration into rendering frameworks is illustrated through the generation of images depicting iridal chromatic variations. Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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