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Two view geometry
In this section the following question will be addressed: Given an image point in one image, does this restrict the position of the corresponding image point in another image? It turns out that it does and that this relationship can be obtained from the calibration or even from a set of prior point correspondences.
Although the exact position of the scene point
is not known, it is bound to be on the line of sight of the corresponding image point
. This line can be projected in another image and the corresponding point
is bound to be on this projected line
. This is illustrated in Figure 3.5.
Figure 3.5:
Correspondence between two views. Even when the exact position of the 3D point
corresponding to the image point
is not known, it has to be on the line through
which intersects the image plane in
. Since this line projects to the line
in the other image, the corresponding point
should be located on this line. More generally, all the points located on the plane defined by
,
and
have their projection on
and
.
 |
In fact all the points on the plane
defined by the two projection centers and
have their image on
. Similarly, all these points are projected on a line
in the first image.
and
are said to be in epipolar correspondence (i.e. the corresponding point of every point on
is located on
, and vice versa).
Every plane passing through both centers of projection
and
results in such a set of corresponding epipolar lines, as can be seen in Figure 3.6. All these lines pass through two specific points
and
. These points are called the epipoles, and they are the projection of the center of projection in the opposite image.
Figure 3.6:
Epipolar geometry. The line connecting
and
defines a bundle of planes. For every one of these planes a corresponding line can be found in each image, e.g. for
these are
and
. All 3D points located in
project on
and
and thus all points on
have their corresponding point on
and vice versa. These lines are said to be in epipolar correspondence. All these epipolar lines must pass through
or
, which are the intersection points of the line
with the retinal planes
and
respectively. These points are called the epipoles.
 |
This epipolar geometry can also be expressed mathematically. The fact that a point
is on a line
can be expressed as
. The line passing trough
and the epipole
is
![\begin{displaymath}
{\tt l} \sim [{\tt e}]_\times {\tt m} \, ,
\end{displaymath}](img401.gif) |
(C23) |
with
the antisymmetric
matrix representing the vectorial product with
.
From (3.9) the plane
corresponding to
is easily obtained as
and similarly
. Combining these equations gives:
 |
(C24) |
with
indicating the Moore-Penrose pseudo-inverse. The notation
is inspired by equation (2.7). Substituting (3.23) in (3.24) results in
Defining
, we obtain
 |
(C25) |
and thus,
 |
(C26) |
This matrix
is called the fundamental matrix. These concepts were introduced by Faugeras [44] and Hartley [60]. Since then many people have studied the properties of this matrix (e.g. [102,103]) and a lot of effort has been put in robustly obtaining this matrix from a pair of uncalibrated images [195,196,225].
Having the calibration,
can be computed and a constraint is obtained for corresponding points. When the calibration is not known equation (3.26) can be used to compute the fundamental matrix
. Every pair of corresponding points gives one constraint on
. Since
is a
matrix which is only determined up to scale, it has
unknowns. Therefore 8 pairs of corresponding points are sufficient to compute
with a linear algorithm.
Note from (3.25) that
, because
. Thus,
. This is an additional constraint on
and therefore 7 point correspondences are sufficient to compute
through a nonlinear algorithm.
In Section 4.3 the robust computation of the fundamental matrix from images will be discussed in more detail.
Subsections
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Marc Pollefeys
2000-07-12