Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
On a differential equation approach to the weighted orthogonal Procrustes problem
Statistics and Computing
Analysis of 3-D Rotation Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relative Pose Calibration Between Visual and Inertial Sensors
International Journal of Robotics Research
Trajectory fusion for three-dimensional volume reconstruction
Computer Vision and Image Understanding
Three-Dimensional planar profile registration in 3d scanning
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Optimal computation of 3-D similarity: Gauss-Newton vs. Gauss-Helmert
Computational Statistics & Data Analysis
Diffeomorphic metric mapping of hybrid diffusion imaging based on BFOR signal basis
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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The well-known Procrustes method determines the optimal rigid body motion that registers two point clouds by minimizing the square distances of the residuals. In this paper, we perform the first order error analysis of this method for the 3D case, fully specifying how directional noise in the point clouds affects the estimated parameters of the rigid body motion. These results are much more specific than the error bounds which have been established in numerical analysis. We provide an intuitive understanding of the outcome to facilitate direct use in applications.