Multi-Camera Network Calibration with a Non-Planar Target
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
The rapid calibration of multi-camera systems using a planar target is typically impractical due to the difficulty of viewing the target in each camera simultaneously. To address these short-comings, a complete calibration methodology using a novel non-planar target for rapid calibration of inward-looking visual sensor networks (VSNs) is presented. We discuss the practical limitations of the approach, arising from an analysis of implementation issues when using spheres as calibration targets such as target-to-target and target-to-camera orientation relationships. This procedure is applied to fully calibrate (intrinsic and extrinsic camera parameters) a twelve camera inward-looking VSN, using only a single image per camera. Results from the calibration are compared for nominal and measured dimensions of the target.
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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 | 0.000 | 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