Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Towards a General Multi-View Registration Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast global registration of 3D sampled surfaces using a multi-z-buffer technique
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
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
A pipeline for building 3D models using depth cameras
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Verification of multi-view point-cloud registration for spherical harmonic cross-correlation
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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Although the Iterative Closest Point (ICP) algorithm has been an extremely popular method for 3D points or surface registration, it can only be applied to two point sets at a time. By only registering two scans at a time, ICP fails to exploit the redundant information available in multiple scans that have overlapping regions. In this paper, we present a multi-view extension of the ICP algorithm by a method that simultaneously averages the redundant information available in the scans with overlapping regions. Variants of this method that carry out such simultaneous registration in a causal manner and that utilise the transitive property of point correspondences are also presented. The improved accuracy of this motion averaged approach in comparison with ICP and some multi-view methods is established through multiple tests. We also present results of our method applied to some well-known real datasets.