A Comparison of Three Geometric Self-Calibration Methods for Range Cameras
Bibliographic record
Abstract
Significant instrumental systematic errors are known to exist in data captured with range cameras using lock-in pixel technology. Because they are independent of the imaged object scene structure, these errors can be rigorously estimated in a self-calibrating bundle adjustment procedure. This paper presents a review and a quantitative comparison of three methods for range camera self-calibration in order to determine which, if any, is superior. Two different SwissRanger range cameras have been calibrated using each method. Though differences of up to 2 mm (in object space) in both the observation precision and accuracy measures exist between the methods, they are of little practical consequence when compared to the magnitude of these measures (12 mm to 18 mm). One of the methods was found to underestimate the principal distance but overestimate the rangefinder offset in comparison to the other two methods whose estimates agreed more closely. Strong correlations among the rangefinder offset, periodic error terms and the camera position co-ordinates are indentified and their cause explained in terms of network geometry and observation range.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".