caa a22 4500 46791432X CHVBK 20180406152857.0 cr unu---uuuuu 170328e20060901xx s 000 0 eng 10.1007/s11263-006-8113-4 doi (NATIONALLICENCE)springer-10.1007/s11263-006-8113-4 Affine Approximation for Direct Batch Recovery of Euclidian Structure and Motion from Sparse Data [Elektronische Daten] [Nicolas Guilbert, Adrien Bartoli, Anders Heyden] We present a batch method for recovering Euclidian camera motion from sparse image data. The main purpose of the algorithm is to recover the motion parameters using as much of the available information and as few computational steps as possible. The algorithm thus places itself in the gap between factorisation schemes, which make use of all available information in the initial recovery step, and sequential approaches which are able to handle sparseness in the image data. Euclidian camera matrices are approximated via the affine camera model, thus making the recovery direct in the sense that no intermediate projective reconstruction is made. Using a little known closure constraint, the FA-closure, we are able to formulate the camera coefficients linearly in the entries of the affine fundamental matrices. The novelty of the presented work is twofold: Firstly the presented formulation allows for a particularly good conditioning of the estimation of the initial motion parameters but also for an unprecedented diversity in the choice of possible regularisation terms. Secondly, the new autocalibration scheme presented here is in practice guaranteed to yield a Least Squares Estimate of the calibration parameters. As a bi-product, the affine camera model is rehabilitated as a useful model for most cameras and scene configurations, e.g. wide angle lenses observing a scene at close range. Experiments on real and synthetic data demonstrate the ability to reconstruct scenes which are very problematic for previous structure from motion techniques due to local ambiguities and error accumulation. Springer Science + Business Media, LLC, 2006 structure from motion nationallicence batch recovery nationallicence closure constraints nationallicence affine camera model nationallicence autocalibration nationallicence contraction mapping nationallicence Guilbert Nicolas Centre for Mathematical Sciences, Lund University, Sweden aut Bartoli Adrien LASMEA (CNRS/Université Blaise Pascal), Clermont-Ferrand, France aut Heyden Anders Division of Mathematics, Malmö University, Sweden aut International Journal of Computer Vision Kluwer Academic Publishers 69/3(2006-09-01), 317-333 0920-5691 69:3<317 2006 69 11263 https://doi.org/10.1007/s11263-006-8113-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-8113-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Guilbert Nicolas Centre for Mathematical Sciences, Lund University, Sweden aut NATIONALLICENCE 700 1- Bartoli Adrien LASMEA (CNRS/Université Blaise Pascal), Clermont-Ferrand, France aut NATIONALLICENCE 700 1- Heyden Anders Division of Mathematics, Malmö University, Sweden aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 69/3(2006-09-01), 317-333 0920-5691 69:3<317 2006 69 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer