I’m also take one lesson on Computer Vision in this semester. The homeworks are not difficult but practical.
Here is output of the 5th. homework.
The main task is to do both Projective Reconstruction and Euclidean Reconstruction given corresponding point pairs and some control points in euclidean coordinates.
In Projective Reconstruction we firstly calculate the fundamental matrix from the given point pairs and then derivate the projective matrices using normalized configuration (the most simple way) . With all this information a linear triangulation method is used to calculate the corresponding object points in projective space. And those procedure is so called Projective Reconstruction (of course the simple without any optimization).
In Euclidean Reconstruction we simply using all the information we got from the Projective Reconstruction and make the same procedure to the control points. At the mean time we calculate the spatial homograph using the control points. Finally all the object points in projective space are transposed into euclidean space using the same spatial homograph, and that is so called Euclidean Reconstruction (without any optimization).
My document(PDF format) of this homework is below, if you have interest, maybe you can read it.