An alternative way to simplify the SfM problem is to consider a
different projection model. The perspective projection case is
characteristic of real cameras however, the corresponding equations
are difficult to deal with. The orthographic case in
Figure 6 greatly simplifies
projection into the almost trivial form ** u=X** and

One orthographic approach which has gained popularity is the
factorization method proposed by Tomasi and Kanade
[55]. Once again, the result is a *linear*
formulation however the linearity is fundamentally different from the
one induced in the previous epipolar geometry approaches. The
technique begins with ** P** tracked feature points over

The algorithm is robust in many situations however it is tuned for orthographic projection, not for perspective effects. Degeneracies may occur when the camera translates forward and this forward motion parameter is not recovered by the system. Only two image-plane translations, camera yaw, roll and pitch are estimated. Therefore, it may not be applicable in some situations. The factorization method has subsequently been extended by Poelman and Kanade to the paraperspective case which is a closer approximation to perspective projection than orthographic projection [44].