A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D registration of partially overlapping surfaces using a volumetric approach
Image and Vision Computing
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Soft Computing - A Fusion of Foundations, Methodologies and Applications
A mean field annealing approach to accurate free form shape matching
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WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
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ACM SIGGRAPH 2007 papers
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3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Constraints for closest point finding
Pattern Recognition Letters
International Journal of Computer Vision
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition
International Journal of Computer Vision
Automatic 3d free form shape matching using the graduated assignment algorithm
Pattern Recognition
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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This paper formalizes overlapping free form shape registration as a minimization problem, which minimizes a weighted sum of registration errors of tentative correspondences with the weights subject to boundary conditions. The tentative correspondences are established using the traditional closest point criterion. Then the powerful barrier method is employed to transform the constrained minimization problem to an unconstrained one. Setting the first order derivative of the unconstrained objective function to zero results in the weights being solved with a closed form solution. The weights are finally globally optimized using the deterministic annealing scheme. Outliers due to occlusion, appearance and disappearance of points in either free form shape are explicitly modelled using a constant. The camera motion parameters are updated in the weighted least squares sense. A comparative study based on both synthetic data and real images shows that the proposed algorithm is promising for the accurate and robust automatic registration of overlapping free form shapes.