Automatic Calibration of a Multiple-Projector Spherical Fish Tank VR Display
Why this work is in the frame
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Bibliographic record
Abstract
We describe a novel automatic calibration method using a single camera for a multiple-projector spherical Fish Tank Virtual Reality (FTVR) display. Modeling the projector as an inverse camera, we estimate the intrinsic and extrinsic projector parameters automatically using a set of projected images on the spherical screen. A calibrated camera is placed beneath to observe partially visible projected patterns. Using the correspondence between the observed pattern and the projected pattern, we reconstruct the shape of the spherical display and finally recover the 3D position of each projected pixel on the display. Additionally we present a practical calibration evaluation method that estimates on-surface accuracy using the single camera. We use point mismatch as a metric to describe misalignment and line mismatch to describe distortion. We demonstrate our automatic approach can achieve an on-surface point mismatch less than 1mm and line mismatch less than 1 on a 30cm diameter spherical screen. Taken together, our calibration approach and evaluation method are automatic and accurate for a desktop spherical FTVR and can be applied to other multiple-projector displays with curved screens.
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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 | 0.001 | 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