MODELLING WIDE-ANGLE LENS CAMERAS FOR METROLOGY AND MAPPING APPLICATIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Wide-angle lenses typically offer fields of view greater than 70°, which are utilized in a variety of imaging, mapping, and navigation applications. Wide-angle lenses are commonly modelled using the central perspective model, compensating for lens distortions through a series of additional parameters. The more extreme the distortions, the further the reality of the lens matches the collinearity equations that define the central perspective model. Fisheye lenses are modelled differently because their fields of view are so wide (typically 180°) that the collinearity model is not applicable. This work studied the effects of modelling wide-angle lenses using both the conventional central perspective model and the fisheye model to determine which model best fits the observations and models the distortions more precisely and accurately. These results were produced by generating observations in a dedicated indoor calibration facility at the University of Calgary: an 11 m × 11 m × 4 m field comprising 291 signalized photogrammetry targets. Multiple free-network, self-calibrating bundle adjustments were performed using different models and different cameras. The results of the self-calibrating bundle adjustments were then utilized in a check adjustment on independent sets of check images to validate their accuracy. Two cameras, a Ladybug5 and a GoPro Hero5, were tested. The GoPro was also calibrated using a checkerboard target pattern, and the results were compared to those of the 3D calibration target-field. The results of the bundle adjustments determined that the fisheye model describes the distortions more precisely in both wide-angle camera systems.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it