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
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Global Solution to Sparse Correspondence Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Formulation of Invariant Point Pattern Matching
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Matching by Linear Programming and Successive Convexification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Branch-and-Bound Methods for Euclidean Registration Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of local image descriptors for full 6 degree-of-freedom pose estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Rotation invariant non-rigid shape matching in cluttered scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Point Set Registration: Coherent Point Drift
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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The well-known robust point matching (RPM) method uses deterministic annealing for optimization, and it has two problems. First, it cannot guarantee the global optimality of the solution and tends to align the centers of two point sets. Second, deformation needs to be regularized to avoid the generation of undesirable results. To address these problems, in this paper we first show that the energy function of RPM can be reduced to a concave function with very few non-rigid terms after eliminating the transformation variables and applying linear transformation; we then propose to use concave optimization technique to minimize the resulting energy function. The proposed method scales well with problem size, achieves the globally optimal solution, and does not need regularization for simple transformations such as similarity transform. Experiments on synthetic and real data validate the advantages of our method in comparison with state-of-the-art methods.