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
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Simultaneous registration of multiple range views for use in reverse engineering of CAD models
Computer Vision and Image Understanding - Special issue on CAD-based computer vision
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Global registration of multiple 3D point sets via optimization-on-a-manifold
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Optimisation-on-a-manifold for global registration of multiple 3D point sets
International Journal of Intelligent Systems Technologies and Applications
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Rigid and Articulated Point Registration with Expectation Conditional Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D modeling using a statistical sensor model and stochastic search
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Structure from motion for scenes with large duplicate structures
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multiview registration of 3D scenes by minimizing error between coordinate frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient and Accurate Method for the Relaxation of Multiview Registration Error
IEEE Transactions on Image Processing
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The registration of multiple 3D structures in order to obtain a full-side representation of a scene is a long-time studied subject. Even if the multiple pairwise registrations are almost correct, usually the concatenation of them along a cycle produces a non-satisfactory result at the end of the process due to the accumulation of the small errors. Obviously, the situation can still be worse if, in addition, we have incorrect pairwise correspondences between the views. In this paper, we embed the problem of global multiple views registration into a Bayesian framework, by means of an Expectation-Maximization (EM) algorithm, where pairwise correspondences are treated as missing data and, therefore, inferred through a maximum a posteriori (MAP) process. The presented formulation simultaneously considers uncertainty on pairwise correspondences and noise, allowing a final result which outperforms, in terms of accuracy and robustness, other state-of-the-art algorithms. Experimental results show a reliability analysis of the presented algorithm with respect to the percentage of a priori incorrect correspondences and their consequent effect on the global registration estimation. This analysis compares current state-of-the-art global registration methods with our formulation revealing that the introduction of a Bayesian formulation allows reaching configurations with a lower minimum of the global cost function.