 
 
 
 
 
 
 
  
Simple parameter counting shows that we have 2nm independent
measurements and only 
11 m + 3 n unknowns, so with enough points and
images the problem ought to be soluble. However, the solution can
never be unique as we always have the freedom to change the 3D
coordinate system we use. In fact, in homogeneous coordinates the
equations become
 transformation H to both projections
transformation H to both projections 
 and world
points
and world
points 
 .
Hence, without further constraints,
reconstruction is only ever possible up to an unknown projective
deformation of the 3D world. However, modulo this fundamental
ambiguity, the solution is in general unique.
.
Hence, without further constraints,
reconstruction is only ever possible up to an unknown projective
deformation of the 3D world. However, modulo this fundamental
ambiguity, the solution is in general unique.
One simple way to obtain the solution is to work in a projective basis tied to the 3D points [3]. Five of the visible points (no four of them coplanar) can be selected for this purpose.
An alternative to this is to select the projection center of the first camera as the coordinate origin, the projection center of the second camera as the unit point, and complete the basis with three other visible 3D points A1, A2, A3 such that no four of the five points are coplanar.
Let 
a1, a2, a3 and 
a'1, a'2, a'3 respectively be
the projections in image 1 and image 2 of the 3D points 
A1, A2,
A3.  Make a projective transformation of each image so that these
three points and the epipoles become a standard basis:
 
 
 
Since the projection matrices are now known, 3D reconstruction is relatively straightforward. This is just a simple, tutorial example so we will not bother to work out the details. In any case, for precise results, a least squares fit has to be obtained starting from this initial algebraic solution (e.g. by bundle adjustment).
 
 
 
 
 
 
