Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Uncalibrated Perspective Reconstruction of Deformable Structures
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Closed-Form Solution to Non-Rigid Shape and Motion Recovery
International Journal of Computer Vision
Non-Rigid Metric Shape and Motion Recovery from Uncalibrated Images Using Priors
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Implicit Non-Rigid Structure-from-Motion with Priors
Journal of Mathematical Imaging and Vision
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perspective Nonrigid Shape and Motion Recovery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Generic and real-time structure from motion using local bundle adjustment
Image and Vision Computing
Image and Vision Computing
A unified view on deformable shape factorizations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
3D reconstruction of non-rigid surfaces in real-time using wedge elements
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Recursive non-rigid structure from motion with online learned shape prior
Computer Vision and Image Understanding
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So far the Non-Rigid Structure-from-Motion problem has been tackled using a batch approach. All the frames are processed at once after the video acquisition takes place. In this paper we propose an incremental approach to the estimation of deformable models. Image frames are processed online in a sequential fashion. The shape is initialised to a rigid model from the first few frames. Subsequently, the problem is formulated as a model based camera tracking problem, where the pose of the camera and the mixing coefficients are updated every frame. New modes are added incrementally when the current model cannot model the current frame well enough. We define a criterion based on image reprojection error to decide whether or not the model must be updated after the arrival of a new frame. The new mode is estimated performing bundle adjustment on a window of frames. To represent the shape, we depart from the traditional explicit low-rank shape model and propose a variant that we call the 3D-implicit low-rank shape model. This alternative model results in a simpler formulation of the motion matrix and provides the ability to represent degenerate deformation modes. We illustrate our approach with experiments on motion capture sequences with ground truth 3D data and with real video sequences.