Authors:
Guillaume Gelabert
;
Michel Devy
and
Frédéric Lerasle
Affiliation:
CNRS; LAAS; Université de Toulouse; UPS, INSA, INPT, ISAE, France
Keyword(s):
Self-calibration, Focal estimation, 3D reconstruction, Bundle adjustment.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
During the two last decades, many contributions have been proposed on 3D reconstruction from image sequences. Nevertheless few practical applications exist, especially using vision. We are concerned by the analysis of image sequences acquired during crash tests. In such tests, it is required to extract 3D measurements about motions of objects, generally identified by specific markings. With numerical cameras, it is quite simple to acquire video sequences, but it is very difficult to obtain from operators in charge of these acquisitions, the camera parameters and their relative positions when using a multicamera system. In this paper, we are interested on the simplest situation: two cameras observing the motion of an object of interest: the challenge consists in reconstructing the 3D model of this object, estimating in the same time, the intrinsic and extrinsic parameters of these cameras. So this paper copes with 3D Euclidean reconstruction with uncalibrated cameras: we recall some
theoretical results in order to evaluate what are the possible estimations when using only two images acquired by two distinct perspective cameras. Typically it will be the two first images of our sequences. It is presented several contributions of the state of the art on these topics, and then results obtained from synthetic data, so that we could state on advantages and drawbacks of several parameter estimation strategies, based on the Sparse Bundle Adjustment and on the Levenberg-Marquardt optimization function.
(More)