© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ ; In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of non-rigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated. An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current non-rigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth. ; Peer Reviewed ; Postprint (author's final draft)


    Access

    Download


    Export, share and cite



    A Bayesian approach to simultaneously recover camera pose and non-rigid shape from monocular images

    Moreno-Noguer, F. / Porta, J. M. | British Library Online Contents | 2016


    Monocular Pose of a Rigid Body Using Point Landmarks

    Krishnan, R. / Sommer, H. J. / Spidaliere, P. D. | British Library Online Contents | 1992



    3D Face pose estimation and tracking from a monocular camera

    Ji, Q. | British Library Online Contents | 2002


    Navigation data extraction from monocular camera images during final approach

    Hiba, Antal / Szabo, Andras / Zsedrovits, Tamas et al. | IEEE | 2018