Camera calibration and relative pose estimation from gravity
Peter Sturm,Long Quan +1 more
- 03 Sep 2000
- Vol. 1, pp 72-75
TL;DR: It is shown that it is possible to estimate the infinite homography and the epipolar geometry between pairs of views from this input, from which the authors can estimate (some) intrinsic parameters and relative pose.
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Abstract: We examine the potential use of gravity for camera calibration and pose estimation purposes. Concretely, objects being launched or dropped follow trajectories dictated by the law of gravity. We examine if video sequences of such trajectories give us exploitable constraints for estimating the imaging geometry. It is shown that it is possible to estimate the infinite homography and the epipolar geometry between pairs of views from this input, from which we can estimate (some) intrinsic parameters and relative pose. There are less singularities compared to approaches that do not use the information that the observed trajectories follow gravity. In this paper, we sketch the geometric principles of our idea and validate them by numerical simulations.
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Citations
Geometric calibration of digital cameras through multi-view rectification
TL;DR: A new and very effective method for high-precision geometric calibration of digital cameras by extracting features with subpixel accuracy from various views of a planar calibration plate and mapping these feature sets into the corresponding points of the undistorted and rectified image that would be generated by an ideal pinhole digital camera.
67
Rapid and brief communication: Camera calibration with one-dimensional objects moving under gravity
TL;DR: It is shown that a 1D object with three or more markers, rotating around one marker which is moving in a plane, provides constraint equations on camera intrinsic parameters, and a stick moving under gravity without other forces acting on performs such a motion.
41
Constraints on general motions for camera calibration with one-dimensional objects
TL;DR: By avoiding singularities, the precision and robustness of the method are improved: the relative mean errors are reduced to less than 5% at the noise level of one pixel which surpasses the state-of-the-art methods of the same category.
31
Visual Learning in Surveillance Systems
Dimitrios Makris
- 01 Jan 2001
TL;DR: This work presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and calibrating cameras in a surveillance system.
8
•Dissertation
Recherches en vision par ordinateur
Peter Sturm
- 16 May 2006
TL;DR: In this paper, mes activites professionnelles, pour la periode allant de 1998 a 2005, were decried, along with mes activities d'animation de la recherche (organisations de colloques, participations a des comites de programme, responsabilites scientifiques, participation a des projets, communications invitees etc.).
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