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
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Robust motion and correspondence of noisy 3-D point sets with missing data
Pattern Recognition Letters
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
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
Convex Optimization
Computer Vision and Image Understanding
Registration of point cloud data from a geometric optimization perspective
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes
International Journal of Computer Vision
Example-based 3D scan completion
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
4-points congruent sets for robust pairwise surface registration
ACM SIGGRAPH 2008 papers
Computer Aided Geometric Design
Global correspondence optimization for non-rigid registration of depth scans
SGP '08 Proceedings of the Symposium on Geometry Processing
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Realtime performance-based facial animation
ACM SIGGRAPH 2011 papers
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations and Trends® in Machine Learning
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
IEEE Transactions on Information Theory
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
IEEE Transactions on Visualization and Computer Graphics
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Rigid registration of two geometric data sets is essential in many applications, including robot navigation, surface reconstruction, and shape matching. Most commonly, variants of the Iterative Closest Point (ICP) algorithm are employed for this task. These methods alternate between closest point computations to establish correspondences between two data sets, and solving for the optimal transformation that brings these correspondences into alignment. A major difficulty for this approach is the sensitivity to outliers and missing data often observed in 3D scans. Most practical implementations of the ICP algorithm address this issue with a number of heuristics to prune or reweight correspondences. However, these heuristics can be unreliable and difficult to tune, which often requires substantial manual assistance. We propose a new formulation of the ICP algorithm that avoids these difficulties by formulating the registration optimization using sparsity inducing norms. Our new algorithm retains the simple structure of the ICP algorithm, while achieving superior registration results when dealing with outliers and incomplete data. The complete source code of our implementation is provided at http://lgg.epfl.ch/sparseicp.