Partial and approximate symmetry detection for 3D geometry
ACM SIGGRAPH 2006 Papers
Nonlinear Mean Shift over Riemannian Manifolds
International Journal of Computer Vision
Estimation of the epipole using optical flow at antipodal points
Computer Vision and Image Understanding
Automatic registration for articulated shapes
SGP '08 Proceedings of the Symposium on Geometry Processing
Vision-based estimation of three-dimensional position and pose of multiple underwater vehicles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
International Journal of Computer Vision
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Journal of Multivariate Analysis
Covariance tracking via geometric particle filtering
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Rotation averaging with application to camera-rig calibration
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Advances in matrix manifolds for computer vision
Image and Vision Computing
Semi-intrinsic mean shift on riemannian manifolds
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Manifold statistics for essential matrices
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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We propose a new method to estimate multiple rigid motions from noisy 3D point correspondences in the presence of outliers. The method does not require prior specification of number of motion groups and estimates all the motion parameters simultaneously. We start with generating samples from the rigid motion distribution. The motion parameters are then estimated via mode finding operations on the sampled distribution. Since rigid motions do not lie on a vector space, classical statistical methods can not be used for mode finding. We develop a mean shift algorithm which estimates modes of the sampled distribution using the Lie group structure of the rigid motions. We also show that proposed mean shift algorithm is general and can be applied to any distribution having a matrix Lie group structure. Experimental results on synthetic and real image data demonstrate the superior performance of the algorithm.