The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
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
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
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
ICP Registration Using Invariant Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Line Geometry
Special issue on registration and fusion of range images
Computer Vision and Image Understanding - Registration and fusion of range images
Multi-level partition of unity implicits
ACM SIGGRAPH 2003 Papers
From curve design algorithms to the design of rigid body motions
The Visual Computer: International Journal of Computer Graphics
Computer Vision and Image Understanding
The correspondence framework for 3D surface matching algorithms
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
Reassembling fractured objects by geometric matching
ACM SIGGRAPH 2006 Papers
Bayesian surface reconstruction via iterative scan alignment to an optimized prototype
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
Pairwise Matching of 3D Fragments Using Cluster Trees
International Journal of Computer Vision
Constraints for closest point finding
Pattern Recognition Letters
Discovering structural regularity in 3D geometry
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH 2008 papers
Freeform surfaces from single curved panels
ACM SIGGRAPH 2008 papers
Registration of combined range-intensity scans: Initialization through verification
Computer Vision and Image Understanding
Simultaneous registration of multiple views with markers
Computer-Aided Design
Multi-sensor calibration through iterative registration and fusion
Computer-Aided Design
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Stochastic Optimization for Rigid Point Set Registration
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Global correspondence optimization for non-rigid registration of depth scans
SGP '08 Proceedings of the Symposium on Geometry Processing
Robust 3D object registration without explicit correspondence using geometric integration
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Robust wide baseline scene alignment based on 3D viewpoint normalization
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
Stochastic global optimization for robust point set registration
Computer Vision and Image Understanding
3D model based tracking for omnidirectional vision: A new spherical approach
Robotics and Autonomous Systems
O-snap: Optimization-based snapping for modeling architecture
ACM Transactions on Graphics (TOG)
Fully Automatic Registration of Image Sets on Approximate Geometry
International Journal of Computer Vision
Sparse iterative closest point
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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The computation of a rigid body transformation which optimally aligns a set of measurement points with a surface and related registration problems are studied from the viewpoint of geometry and optimization. We provide a convergence analysis for widely used registration algorithms such as ICP, using either closest points (Besl and McKay, 1992) or tangent planes at closest points (Chen and Medioni, 1991) and for a recently developed approach based on quadratic approximants of the squared distance function (Pottmann et al., 2004). ICP based on closest points exhibits local linear convergence only. Its counterpart which minimizes squared distances to the tangent planes at closest points is a Gauss---Newton iteration; it achieves local quadratic convergence for a zero residual problem and--if enhanced by regularization and step size control--comes close to quadratic convergence in many realistic scenarios. Quadratically convergent algorithms are based on the approach in (Pottmann et al., 2004). The theoretical results are supported by a number of experiments; there, we also compare the algorithms with respect to global convergence behavior, stability and running time.