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About this paper
Chaehyeon Song, Dongjae Lee, Jongwoo Lim, Ayoung Kim
An uncertainty-aware calibration framework uses circular target patterns, an unbiased distorted-conic projection model, and correlated contour statistics to improve camera parameter estimation. Across synthetic, RGB, and thermal-infrared experiments, the full method reduces calibration failures and parameter variation, achieving 0.40-pixel reprojection error on thermal data and focal-length estimates within about 0.1 pixels of the synthetic ground truth under motion blur.
Key insights
- Connected-contour modeling captures shape structure more faithfully than independent boundary-point assumptions; stronger gradients and more boundary samples generally produce more certain centroids.
- Correcting projection bias is essential: in synthetic tests, adding the unbiased estimator moved focal-length estimates from roughly 604 to 600.2 pixels when the ground truth was 600 pixels.
- Calibration quality depends strongly on diversity of target orientations, not only on distributing observations spatially across the image.
Summary by alphaXiv.
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