I have two point sets of about 800 3D-points that are matched. That means, I know the corresponding points in the two point clouds. Now I want to minimize the distance between the corresponding points using a projective transformation because I know that the transformation can't be completly described by an affine transformation. So after using an affine transformation I still have an error that is a little too high.
Now my question is how I can compute a viable projective transformation that maps the points of one set to the points of the other set? I know how to calculate least square estimates of a rigid transformation and of an affine transformation but I can't find any sources how to handle two large point sets and compute something like a least squares solution of a projective transform. Is that possible?
At the moment my idea is the following: 1. Compute an affine transformation, using the least squares solution of:
$ \begin{pmatrix} x' \\ y' \\ z' \\ 1 \end{pmatrix} = \begin{pmatrix} a_1 & a_2 & a_3 & a_4 \\ b_1 & b_2 & b_3 & b_4 \\ c_1 & c_2 & c_3 & c_4 \\ 0 & 0 & 0 & 1 \end{pmatrix} \cdot \begin{pmatrix} x \\ y \\ z \\ 1 \end{pmatrix} $
This matrix is used as an start vector for a minimization procedure using levenberg-marquardt on the following matrix:
$ T = \begin{pmatrix} a_1 & a_2 & a_3 & a_4 \\ b_1 & b_2 & b_3 & b_4 \\ c_1 & c_2 & c_3 & c_4 \\ d_1 & d_2 & d_3 & 1 \end{pmatrix} $
The idea is to minimize the cost function of the reprojection error:
$ \sum \| T\cdot x_i - x_i' \| $
The problem in my eyes is that I don't use a parametrization of the matrix that preserves non singularity in the optimization process which could be a problem because projective transformation matrices have to be non singular and in addition I thought that I don't need a non-linear optimization routine (because the projective transformation is still linear)! Do you think this approach is viable or is there any better and easier way to determine a projective transformation between two 3D-point sets?
Actually I know that I only have to solve the following equation system:
$
\begin{pmatrix}
wx' \\
wy' \\
wz' \\
w
\end{pmatrix}
=
\begin{pmatrix}
a_1 & a_2 & a_3 & a_4 \\
b_1 & b_2 & b_3 & b_4 \\
c_1 & c_2 & c_3 & c_4 \\
d_1 & d_2 & d_3 & 1
\end{pmatrix}
\cdot
\begin{pmatrix}
x \\
y \\
z \\
1
\end{pmatrix}
$
So the difference to the affine transformations are the homogeneous coordinates. But how is this possible in a sense of regarding 800 points in a least squares way?
Best regards!
