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
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
An Orientation Reliability Matrix for the Iterative Closest Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICP Registration Using Invariant Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Optimization
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
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
The correspondence framework for 3D surface matching algorithms
Computer Vision and Image Understanding
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
Fully Automatic Registration of 3D Point Clouds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Part-Based Probabilistic Point Matching
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Dense 3D reconstruction from images by normal aided matching
Machine Graphics & Vision International Journal
A New Affine Registration Algorithm for Matching 2D Point Sets
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
2D and 3D face recognition: A survey
Pattern Recognition Letters
Cached k-d tree search for ICP algorithms
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Outlier Robust ICP for Minimizing Fractional RMSD
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Registration of combined range-intensity scans: Initialization through verification
Computer Vision and Image Understanding
Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region matching with missing parts
Image and Vision Computing
Geometric Direct Search Algorithms for Image Registration
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Iterative Estimation of Rigid-Body Transformations
Journal of Mathematical Imaging and Vision
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The traditional iterative closest point (ICP) algorithm is accurate and fast for rigid point set registration but it is unable to handle affine case. This paper instead introduces a novel generalized ICP algorithm based on lie group for affine registration of m-D point sets. First, with singular value decomposition technique applied, this paper decomposes affine transformation into three special matrices which are then constrained. Then, these matrices are expressed by exponential mappings of lie group and their Taylor approximations at each iterative step of affine ICP algorithm. In this way, affine registration problem is ultimately simplified to a quadratic programming problem. By solving this quadratic problem, the new algorithm converges monotonically to a local minimum from any given initial parameters. Hence, to reach desired minimum, good initial parameters and constraints are required which are successfully estimated by independent component analysis. This new algorithm is independent of shape representation and feature extraction, and thereby it is a general framework for affine registration of m-D point sets. Experimental results demonstrate its robustness and efficiency compared with the traditional ICP algorithm and the state-of-the-art methods.