Surface modeling with oriented particle systems
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multibody Grouping from Motion Images
International Journal of Computer Vision
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
N-Dimensional Tensor Voting and Application to Epipolar Geometry Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Frame Correspondence Estimation Using Subspace Constraints
International Journal of Computer Vision
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust multi-body motion tracking using commute time clustering
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Multibody grouping is a representative of applying subspace constraints in computer vision tasks. Under linear projection models, feature points of multibody reside in multiple subspaces. We formulate the problem of multibody grouping as multiple subspace inference from highdimensional data space. The theoretical value and practical advantage of this formulation come from the relaxation of the motion independency assumption which has to be enforced in most factorization based methods. In the proposed method, an Oriented-Frame (OF), which is a multidimensional coordinate frame, is associated with each data point indicating the point's preferred subspace structure. Then, a similarity measurement of these OFs is introduced and a novel mechanism is devised for conveying the information of the inherent subspace structure among the data points. In contrast to the existing factorization-based algorithms that can not find correct segmentation of correlated motions such as articulated motion, the proposed method can robustly handle motion segmentation of both independent and correlated cases. Results on controlled and real experiments show the effectiveness of the proposed subspace inference method.