Let
be an affine camera motion
obtained by factoring the measurement matrix. Then
is related to the ``true'' motion
described with respect to the
Euclidean coordinate frame by (9). Our goal here
is to obtain the affine transformation
(up to an arbitrary
rotation) in (9) with which affine descriptions
of
motion and shape are corrected to Euclidean ones
.
Let two 3-vectors
and
denote two rows of
. Then, for each affine camera
model introduced in §2.2, the affine
transformation
is determined as follows;
using
its orthogonality, we have constraints on the affine transformation
called metric constraints:
Let
and
be two elements of
. Then, eliminating
from
(19), we obtain

which gives 2F linear homogeneous equations in terms of 6 elements of
the 3
3 symmetric matrix
. If three or
more views are available, we can obtain a least-squares solution
determined up to a scale as a solution of the ordinary eigenvalue
problem. Once
is determined,
is
recovered through, for instance, Cholesky decomposition.
and the affine transformation
is determined as a
least-squares solution of the following equations:

and
is recovered by solving, in the
least-squares sense,
