Multiple Camera Calibration using Robust Perspective Factorization

Andrei Zaharescu 1 Radu Horaud 1 Rémi Ronfard 2 Loic Lefort 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : In this paper we address the problem of recovering structure and motion from a large number of intrinsically calibrated perspective cameras. We describe a method that combines (1) weak-perspective reconstruction in the presence of noisy and missing data and (2) an algorithm that updates weakperspective reconstruction to perspective reconstruction by incrementally estimating the projective depths. The method also solves for the reversal ambiguity associated with affine factorization techniques. The method has been successfully applied to the problem of calibrating the external parameters (position and orientation) of several multiple-camera setups. Results obtained with synthetic and experimental data compare favourably with results obtained with nonlinear minimization such as bundle adjustment.
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Andrei Zaharescu, Radu Horaud, Rémi Ronfard, Loic Lefort. Multiple Camera Calibration using Robust Perspective Factorization. 3D Data Processing, Visualization and Transmission, 2006, Chapel Hill, United States. pp.504 -511. ⟨inria-00545155⟩

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