A Unifying Model for Camera Calibration

Abstract : This paper proposes a unified theory for calibrating a wide variety of camera models such as pinhole, fisheye, cata-dioptric, and multi-camera networks. We model any camera as a set of image pixels and their associated camera rays in space. Every pixel measures the light traveling along a (half-) ray in 3-space, associated with that pixel. By this definition, calibration simply refers to the computation of the mapping between pixels and the associated 3D rays. Such a mapping can be computed using images of calibration grids, which are objects with known 3D geometry, taken from unknown positions. This general camera model allows to represent non-central cameras; we also consider two special subclasses, namely central and axial cameras. In a central camera, all rays intersect in a single point, whereas the rays are completely arbitrary in a non-central one. Axial cameras are an intermediate case: the camera rays intersect a single line. In this work, we show the theory for calibrating central, axial and non-central models using calibration grids, which can be either three-dimensional or planar.
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Srikumar Ramalingam, Peter Sturm. A Unifying Model for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2017, 39 (7), pp.1309-1319. ⟨10.1109/TPAMI.2016.2592904⟩. ⟨hal-01396809⟩

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