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
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Multidimensional binary search trees used for associative searching
Communications of the ACM
Geometric matching of 3D objects: assessing the range of successful initial configurations
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
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
Multi-Feature Matching Algorithm for Free-Form 3D Surface Registration
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A fast algorithm for ICP-based 3D shape biometrics
Computer Vision and Image Understanding
Model based foot shape classification using 2D foot outlines
Computer-Aided Design
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Shape registration plays an important role in applications such as 3D object modeling or object recognition. The iterative closest point (ICP) algorithm is widely used for the registration of geometric data. One of its main drawback is its time complexity O(N2), quadratic with the shape size N, which implies long processing time, especially when using high resolution data. Several methods were proposed to accelerate the process. One of the most effective one uses a tree search (k-D tree) to establish closest point relationships and reduces the complexity to O(N logN). This paper reviews several of the existing methods and proposes and analyses a new, even less complex ICP algorithm, that uses a heuristic approach to find the closest points. Based on a local search it permits to reduce the complexity to O(N) and to greatly accelerate the process. A comprehensive analysis and a comparison of the considered algorithm with a tree search method are presented.