Algorithm 2 Least squares alignment of two 3D point sets based on SVD
1:	procedure
2:	Let $Y=\{y_1,\cdots ,y_n\}$ and $X=\{x_1,\cdots ,x_n\}$ two sets with corresponding points.
3:	function AlignmentSVD($X,Y$)
4:	$y_0=\frac{1}{n}{\sum }_{i=1}^n{y}_i$	▷ mean of the data point set X
5:	$x_0=\frac{1}{n}{\sum }_{i=1}^n{x}_i$	▷ mean of the data point set Y
6:	$H={\sum }_{i=1}^n(y_i-y_0){(x_i-y_0)}^T$	▷ cross covariance between X and Y
7:	$UD{V}^T=SVD\left(H\right)$	▷ singular value decomposition of H
8:	$\mathbf{R}=V{U}^T$	▷ rotation matrix
9:	$\mathbf{t}=y_0-\mathbf{R}{x}_0$	▷ translation vector
10:	$\mathbf{T}\leftarrow [\mathbf{R},\mathbf{t}]$	▷ extrinsic parameter matrix
11:	return $\mathbf{T}$
12:	end function
13:	end procedure