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
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Registration of point cloud data from a geometric optimization perspective
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
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
Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes
International Journal of Computer Vision
Object Pose Detection in Range Scan Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Unified Continuous Optimization Framework for Center-Based Clustering Methods
The Journal of Machine Learning Research
Optimisation-on-a-manifold for global registration of multiple 3D point sets
International Journal of Intelligent Systems Technologies and Applications
Simultaneous nonrigid registration of multiple point sets and atlas construction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
Hi-index | 0.00 |
Determining Euclidean transformations for the robust registration of noisy unstructured point sets is a key problem of model-based computer vision and numerous industrial applications. Key issues include accuracy of the registration, robustness with respect to outliers and initialization, and computational speed. In this paper, we consider objective functions for robust point registration without correspondence. We devise a numerical algorithm that fully exploits the intrinsic manifold geometry of the underlying Special Euclidean Group SE(3) in order to efficiently determine a local optimum. This leads to a quadratic convergence rate that compensates the moderately increased computational costs per iteration. Exhaustive numerical experiments demonstrate that our approach exhibits significantly enlarged domains of attraction to the correct registration. Accordingly, our approach outperforms a range of state-of-the-art methods in terms of robustness against initialization while being comparable with respect to registration accuracy and speed.