Minimal Solutions for Generic Imaging Models

Srikumar Ramalingam 1 Peter Sturm 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : A generic imaging model refers to a non-parametric camera model where every camera is treated as a set of unconstrained projection rays. Calibration would simply be a method to map the projection rays to image pixels; such a mapping can be computed using plane based calibration grids. However, existing algorithms for generic calibration use more point correspondences than the theoretical minimum. It has been well-established that non-minimal solutions for calibration and structure-from-motion algorithms are generally noise-prone compared to minimal solutions. In this work we derive minimal solutions for generic calibration algorithms. Our algorithms for generally central cameras use 4 point correspondences in three calibration grids to compute the motion between the grids. Using simulations we show that our minimal solutions are more robust to noise compared to non-minimal solutions. We also show very accurate distortion correction results on fisheye images.
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Srikumar Ramalingam, Peter Sturm. Minimal Solutions for Generic Imaging Models. CVPR 2008 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2008, Anchorage, United States. pp.1-8, ⟨10.1109/CVPR.2008.4587710⟩. ⟨inria-00384261⟩

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