[eigen] Transform products

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Hi list,

there still remains a few issues with the product: Transform * matrix_expression

Let d be the dimension of the ambient space (so that the Transform
object actually correspond to a d+1 x d+1 matrix). Currently we allow:

1 - Transform * [d x d] => Transform
2 - Transform * [d+1 x d+1] => trivial product expression
3 - Transform * [d+1 x 1] => trivial product expression
4 - Transform * [d x 1] => complex [d x 1] expression including the
homogeneous normalization


a) the 4-th case is not plenty satisfactory:

 a1 - should it returns an homogeneous vector ?
 a2 - or automatically does the normalization as it currently does ?
 a3 - or should we offer a way to skip the normalization assuming the
transformation is affine (last row = [0 ... 0 1]) ?

Well, these questions are more complementary and I guess the answer is
yes for all, the problem is rather how to expose all these variants ?
a proposal:

for a3 let's add "t.affine() * v"   where affine() would return  a
kind of [d x d+1] proxy with overloaded operator *.

for a1 and a2, two options:

 p1) keep the default as it because it is safe
       and for a1... well I don't know, anyway the user can still
       build and homogeneous one for the rhs.

 p2) let's return a "homogeneous" object which would
      automatically be converted to a [d x 1] vector if needed
      (ideally would have to be done in MatrixBase)

b) second issue: We want to be able to perform a batch transformation
of a set of N vectors. Again two cases:

 b1 - Transform * [d+1 x N] => this is trivial, we just have to merge
the above cases 1 and 2 into a more generic one. DONE

 b2 - Transform * [d x N]  =>  same issues than the ones discussed
above excepted that the involved expressions are much more complicated
! For instance the affine case would be:
  t.matrix().linear() * [d x N] + t.matrix().translation() *

This last example also shows that maybe it would be useful to have a
"replicate" expression mapping a vector to a matrix with constant rows
or constant columns ? This would be simpler than using a matrix
product for that.

And of course all the above discussion also holds for the transpose
cases, i.e., matrix_expression * Transform.

opinion, idea ?


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