computational geometry – Mahalanobis distance of point to plane algorithm

I am trying to understand the Mahalanobis distance of a point from the plane given by this paper. The algorithm is given below:

  1. Calculate covariance of point $S_{uu}$
  2. Apply a whitening transform to the covariance matrix using SVD where $S_{uu} = RVR^T$. $R$ is the rotation matrix and $V$ is the scale $V = diag(a^2, b^2, c^2)$
  3. The output is transformed into 3 homogenous 4×4 matrices

$Rh = begin{bmatrix} R & 0 \ 0^T & 1 end{bmatrix}, Th = begin{bmatrix} I & 0 \ -u^T & 1 end{bmatrix}, Ah = diag(a,b,c,1)$

where u is the centroid of the plane

4.The transformed plane is given by $q = Ah*Rh*Th*p$ where p is the planar parameters

My first question is regarding the whitening transform. I am not sure how it is being derived (its not given in the paper). If anyone can provide an intuitive explanation, that would be great.

My second question is – I simulated a plane with planar parameters (0, 0, 1) and points on the plane with std error of 0.02. enter image description here We can recover the planar parameters using svd. Now, when I run the algorithm to get the transformed plane using the following code:

# whitening transform of point covariance
cov_pts = np.cov(pts.transpose())
r, v, rt = np.linalg.svd(cov_pts)
abc = np.sqrt(v)
ah = np.diag(v=np.concatenate((abc, np.array((1)))))
rh = np.zeros((4, 4))
rh(0:3, 0:3) = r
rh(3, 3) = 1
th = np.eye(4)
th(3, 0:3) = -center
transformed_plane_fit =, rh), th),
                                       np.concatenate((plane_fit, np.array((1)))))

the values I get for transformed planar fit is array((-2.99291157e-02, -7.31159923e-04, -1.97310249e-02, 9.11576594e-01)) which doesn’t seem to correspond to the planar parameters. What can be the reason for the discrepancy?