Bibliographic record
Abstract
Tolerancing a lens is a basic procedure in lens design. It consists in first defining an appropriate set of tolerances for the lens, then in adding compensators with their allowable ranges and finally in selecting an appropriate quality criterion (MTF, RMS spot size, wavefront error, boresight error...) for the given application. The procedure is straightforward for standard optical systems. However, it becomes more complex when tolerancing very wide angle lenses (larger than 150 degrees). With a large field of view, issues such as severe off-axis pupil shift, considerable distortion and low relative illumination must be addressed. The pupil shift affects the raytrace as some rays can no longer be traced properly. For high resolution imagers, particularly for robotic and security applications, the image footprint is most critical in order to limit or avoid complex calibration procedures. We studied various wide angle lenses and concluded that most of the distortion comes from the front surface of the lens. Consequently, any variation of the front surface will greatly affect the image footprint. In this paper, we study the effects on the image footprint of slightly modifying the front surface of four different lenses: a simple double-gauss for comparison, a fisheye lens, a catadioptric system (omnidirectional lens) and a Panomorph lens. We also present a method to analyze variations of the image footprint. Our analysis shows that for wide angle lenses, on which the entrance pupil is much smaller than the front surface, irregularities (amplitude, slope and location) are critical on both aspherical and spherical front surfaces to predict the image footprint variation for high resolution cameras. Finally, we present how the entrance pupil varies (location, size) with the field of view for these optical systems.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".