The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
Towards a General Multi-View Registration Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of four algorithms for estimating 3-D rigid transformations
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Optimal Registration of Object Views Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Simultaneous registration of multiple corresponding point sets
Computer Vision and Image Understanding
Linear combination of transformations
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Analysis of 3-D Rotation Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Solution for the Registration of Multiple 3D Point Sets Using Unit Quaternions
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Fast Automatic Method for Registration of Partially-Overlapping Range Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Registration of Multiple Point Sets
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Global registration of multiple 3D point sets via optimization-on-a-manifold
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Multiview registration of 3D scenes by minimizing error between coordinate frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
Fully Automatic Registration of Image Sets on Approximate Geometry
International Journal of Computer Vision
Computer Methods and Programs in Biomedicine
Bayesian perspective for the registration of multiple 3D views
Computer Vision and Image Understanding
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We propose a novel algorithm to register multiple 3D point sets within a common reference frame simultaneously. Our approach performs an explicit optimisation on the manifold of rotations. Firstly, we present a new closed-form solution for simultaneous multiview registration in the noise-free case. Secondly, we use this as a first step to derive a good initial estimate of a solution in the case of noisy data. This initialisation step may be of use in any general iterative scheme. Finally, we present an iterative scheme based on Gauss Newton method evolving on rotations manifold that has locally quadratic convergence. We demonstrate the efficacy of our scheme on scan data taken both from the Digital Michelangelo Project and from scans extracted from models. In all cases under study, our algorithm converges much faster than the other well-known approaches (in some cases orders of magnitude faster) and generates consistently higher quality registrations.