Suppose that a fixed scene is seen by two or more perspective cameras. We are interested in geometric issues, so we will suppose that the correspondences between visible points in different images are already known. However it must be pointed out that matching is a fundamental and extremely difficult issue in vision, which can not be dismissed so lightly in practice.

So in this section, we suppose that *n* 3D points *A*_{i} are observed by *m* cameras with projection matrices . Neither the point positions nor the camera projections are given. Only the projections *a*_{ij} of the *i*^{th} point in the *j*^{th}image are known.

Simple parameter counting shows that we have 2*nm* 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

So we always have the freedom to apply a nonsingular transformation *H* to both projections 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.

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 *A*_{1}, *A*_{2}, *A*_{3} such that no four of the five points are coplanar.

Let *a*_{1}, *a*_{2}, *a*_{3} and *a*‘_{1}, *a*‘_{2}, *a*‘_{3} respectively be the projections in image 1 and image 2 of the 3D points *A*_{1}, *A*_{2}, *A*_{3}. Make a projective transformation of each image so that these three points and the epipoles become a standard basis:

Also fix the 3D coordinates of *A*_{1}, *A*_{2}, *A*_{3} to be respectively

It follows that the two projection matrices can be written:

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).

Section 4.1 described the advantages of recovering affine space and provided some methods of computing the location of the plane at infinity . The easiest way to proceed is to use prior information, for instance the knowledge that lines in the scene are parallel or that a point is the half way between two others.

Prior constraints on the camera motion can also be used. For example, a translating camera is equivalent to a translating scene. Observing different images of the same point gives a line in the direction of motion. Intersecting several of these lines gives the point at infinity in the motion direction, and hence one constraint on the affine structure.

On the other hand, any line through two scene points translates into a line parallel to itself, and the intersection of these two lines gives further constraints on the affine structure. Given three such point pairs we have in all four points at infinity, and the projective reconstruction of these allows the ideal points to be recovered — see [16] for details and experimental results.

We have not implemented the specific suggestions of section 4.2 for the recovery of Euclidean structure from a projective reconstruction by observing known scene angles, circles,…However we have developed a more brute force approach, finding a projective transformation *H* in equation (5.4) that maps the projective reconstruction to one that satisfies a set of redundant Euclidean constraints. This simultaneously minimizes the error in the projection equations and the violation of the constraints. The equations are highly nonlinear and a good initial guess for the structure is needed. In our experiments this was obtained by assuming parallel projection, which is linear and allows easy reconstruction using SVD decomposition ([26]).

Figures 5.3 and 5.4 show an example of this process.

- Contents
- Foreword and Motivation
- Basic Properties of Projective Space
- Projective Invariants & the Cross Ratio
- A Hierarchy of Geometries
- Projective Stereo vision
- Bibliography

Source:

http://www.cse.iitd.ernet.in/~suban/vision/tutorial/node1.html

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