Structure and motion of nonrigid object under perspective projection
Pattern Recognition Letters
A matter of notation: Several uses of the Kronecker product in 3D computer vision
Pattern Recognition Letters
Towards a measure of deformability of shape sequences
Pattern Recognition Letters
Rotation constrained power factorization for structure from motion of nonrigid objects
Pattern Recognition Letters
Implicit Non-Rigid Structure-from-Motion with Priors
Journal of Mathematical Imaging and Vision
Activity representation using 3D shape models
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Extracting Average Shapes from Occluded Non-rigid Motion
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
International Journal of Computer Vision
Monocular 3-D tracking of inextensible deformable surfaces under L2-norm
IEEE Transactions on Image Processing
Convex optimization for nonrigid stereo reconstruction
IEEE Transactions on Image Processing
Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion
Image and Vision Computing
Rigid Structure from Motion from a Blind Source Separation Perspective
International Journal of Computer Vision
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Non-rigid metric reconstruction from perspective cameras
Image and Vision Computing
Nonrigid stereo reconstruction using linear programming
Proceedings of the 1st international workshop on 3D video processing
Piecewise quadratic reconstruction of non-rigid surfaces from monocular sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Adaptive metric registration of 3D models to non-rigid image trajectories
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Monocular template-based reconstruction of smooth and inextensible surfaces
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
A fast approach to deformable surface 3D tracking
Pattern Recognition
Automatic estimation of the number of deformation modes in non-rigid SfM with missing data
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Monocular Template-based Reconstruction of Inextensible Surfaces
International Journal of Computer Vision
Optimal Metric Projections for Deformable and Articulated Structure-from-Motion
International Journal of Computer Vision
3D model-based face recognition in video
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Deformation weight constraint and 3D reconstruction of nonrigid objects
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A unified view on deformable shape factorizations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Non-rigid self-calibration of a projective camera
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Adaptive Non-rigid Registration and Structure from Motion from Image Trajectories
International Journal of Computer Vision
Multilinear Factorizations for Multi-Camera Rigid Structure from Motion Problems
International Journal of Computer Vision
A Simple Prior-Free Method for Non-rigid Structure-from-Motion Factorization
International Journal of Computer Vision
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The nonrigid structure-from-motion (NSFM) problem seeks to recover a sequence of 3D shapes, shape articulation parameters, and camera view matrices from 2D correspondence data. Factorization approaches relate the principal subspaces of the data matrix to the desired parameters through a linear corrective transform. Current methods for finding this transform are heuristic or depend on strong assumptions about the data. We show how to solve for this transform by directly minimizing deviation from the required orthogonal structure of the projection/articulation matrix. The solution is exact for noiseless data and an order of magnitude more accurate than state-of-the-art methods for noisy data.