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Conference Papers Year : 2020

Stratified Autocalibration of Cameras with Euclidean Image Plane

Abstract

This paper tackles the problem of stratified autocalibration of a moving camera with Euclidean image plane (i.e. zero skew and unit aspect ratio) and constant intrinsic parameters. We show that with these assumptions, in addition to the polynomial derived from the so-called modulus constraint, each image pair provides a new quartic polynomial in the unknown plane at infinity. For three or more images, the plane at infinity estimation is stated as a constrained polynomial optimization problem that can efficiently be solved using Lasserre's hierarchy of semidefinite relaxations. The calibration parameters and thus a metric reconstruction are subsequently obtained by solving a system of linear equations. Synthetic data and real image experiments show that the new polynomial in our proposed algorithm leads to a more reliable performance than existing methods.
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Dates and versions

hal-02939760 , version 1 (15-09-2020)

Identifiers

  • HAL Id : hal-02939760 , version 1

Cite

Devesh Adlakha, Adlane Habed, Fabio Morbidi, Cédric Demonceaux, Michel de Mathelin. Stratified Autocalibration of Cameras with Euclidean Image Plane. The 31st British Machine Vision Conference, Sep 2020, Manchester, United Kingdom. ⟨hal-02939760⟩
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