Requirements for Camera Calibration: Must Accuracy Come with a High Price?
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
Since a large number of vision applications rely on the mapping between 3D scenes and their corresponding 2D camera images, an important practical consideration for researchers is, what are the major determinants of camera calibration accuracy and what accuracy can be achieved within the practical limits of their environments. In response, we present a thorough study investigating the effects of training data quantity, measurement error, pixel coordinate noise, and the choice of camera model, on camera calibration results. Through this effort, we seek to determine whether expensive, elaborate setups are necessary, or indeed, beneficial, to camera calibration, and whether a high complexity camera model leads to improved accuracy. The results are first provided for a simulated camera system and then verified through carefully controlled experiments using real-world measurements
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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