Calibration of multi-camera photogrammetric systems
Bibliographic record
Abstract
Abstract. Due to the low-cost and off-the-shelf availability of consumer grade cameras, multi-camera photogrammetric systems have become a popular means for 3D reconstruction. These systems can be used in a variety of applications such as infrastructure monitoring, cultural heritage documentation, biomedicine, mobile mapping, as-built architectural surveys, etc. In order to ensure that the required precision is met, a system calibration must be performed prior to the data collection campaign. This system calibration should be performed as efficiently as possible, because it may need to be completed many times. Multi-camera system calibration involves the estimation of the interior orientation parameters of each involved camera and the estimation of the relative orientation parameters among the cameras. This paper first reviews a method for multi-camera system calibration with built-in relative orientation constraints. A system stability analysis algorithm is then presented which can be used to assess different system calibration outcomes. The paper explores the required calibration configuration for a specific system in two situations: major calibration (when both the interior orientation parameters and relative orientation parameters are estimated), and minor calibration (when the interior orientation parameters are known a-priori and only the relative orientation parameters are estimated). In both situations, system calibration results are compared using the system stability analysis methodology.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".