Fully automatic multi-projector calibration with an uncalibrated camera
Bibliographic record
Abstract
Multiple video projectors can be used to provide a seamless, undistorted image or video over one or more display surfaces. Correct rendering requires calibration of the projectors with respect to these surface(s) and an efficient mechanism to distribute and warp the frame buffer data to the projectors. Typically, the calibration process involves some degree of manual intervention or embedding of optical sensors in the display surface itself, neither of which is practical for general deployment by non-technical users. We show that an effective result can in fact be achieved without such intervention or hardware augmentation, allowing for a fully automatic multi-projector calibration that requires nothing more than a low-cost uncalibrated camera and the placement of paper markers to delimit the boundaries of the desired display region. Both geometric and intensity calibration are performed by projection of gray-coded binary patterns, observed by the camera. Finally, the frame buffer contents for display are distributed in real time by a remote desktop transport to multiple rendering machines, connected to the various projectors.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".