Abstract
A sequence of algorithms for Euclidian reconstruction of rigid three-dimensional objects from point correspondences is presented. This sequence is part of a monocular 3D real time scanning system, which allows us to acquire Euclidian point-mesh descriptions of objects, and can be used to augment virtual reality. Since the general way to obtain the epipolar, projective, and Euclidian geometries from point feature correspondences is already solved, here the emphasis is on the performance of the algorithms in the presence of noise. Kanatani's (1996) epipolar geometry estimation method is improved and this is experimentally validated. Regarding Bougnoux's (1998) Euclidian geometry estimation method, the initial linear solution is now obtained with less uncertainty and the non-linear minimization no longer converges to a hidden solution.
Published Version
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