Efficient Geometric, Photometric, and Temporal Calibration of an Array of Unsynchronized Video Cameras
Bibliographic record
Abstract
Camera-arrays have become popular in many computer vision and computer graphics applications. Among all preprocessing steps, an efficient method to calibrate a large number of cameras is very much desired. The required calibration includes both the geometric and photometric calibration, which are the most common and also well studied for single camera. However, few existing efforts are devoted to camera arrays or to integrate both methods in a fully automatic way. Additionally, most existing camera array systems require or assume implicitly that all the cameras in the array are hardware-synchronized to simplify subsequent application-specific processing such as the calibration of all the cameras. While this constraint is useful, it greatly restricts the use of heterogeneous types of cameras and the configuration of cameras that could be used. In this paper, we propose a novel integrated and fully automatic solution for performing geometric, photometric and temporal calibration (synchronization) of an array of unsynchronized video cameras. In particular, our new method is based on the classic plane based calibration approach. By using a redesigned calibration pattern, the geometric, photometric and temporal calibrations are done in an integrated and extensible framework automatically. Extensive experimental results show that the new method is very easy to use and can achieve high accuracy in the calibrated parameters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".