Accurate camera performance prediction using optical and imaging simulation pipeline for super wide-angle lens
Bibliographic record
Abstract
Optical design process consists in minimizing aberrations using optimization methods. It relies on key performance indicators (KPIs), such as point spread function (PSF), Modulated transfer function (MTF), or relative illumination (RI) and spot sizes, that depend on lens elements aberrations. Their target values need to be defined -either for human or machine perception- at early stage of the design, which can be complex to do for challenging designs such as extended field of view. We developed an optical and imaging simulation pipeline able to render the effects of complex optical designs and image sensor on an initial aberration-free image. Extracting files from ray tracing software for simulating the PSF and sensor target information, the algorithm accurately renders off-axis aberrations with Zernike polynomials representation combined with noise contribution and relative illumination. The obtained image faithfully represents an optical system performance from the optics to the sensor component and we can then study the impact of additional aberration introduction.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".