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[hcherkaoui <>]

Thanks for the reply  Sebastien, 

So I work on the binding and now it works fine, it pass all my tests and
finally paraview confirm the good reconstruction. I've also bind
Alpha_Shape_3 and like the Poisson process it works fine.

However, I bind these process for my work so especially for Alpha_Shape_3
it's far from the work done in the old project 'cgal-python'
(http://cgal-python.gforge.inria.fr/).
The aim of my work it to provide python module to do this:

python-list-of-Point_3 => | my module (poisson or alpha_shape_3)| =>
python-Polyhedron_3

So my work could interest some peoples but may be not the official
repository of cgal-binding, am I right?
I produced hight-level-functions, to be clear this is the little API
provided by my two bindings:

CGAL.CGAL_Alpha_shape_3 :
        Alpha_shape_3_reconstruction(points, alpha_coef) 
        \points: list of Point_3
        \alpha_coef: python float, coef in front of the optimal value of
alpha (.find_optimal_alpha(1))
                          pass to the .set_alpha() methode
        \return a Polyhedron_3

CGAL.CGAL_Mesh_process_3
        Computes_reconstruction_error(points, Polyhedron)
        \points python-list of Point_3
        \Polyhedron the re-construction polyhedron
        \return a list of 3 python-floats 
                             [avg_distance of the point set, 
                              max distance points set <-> surface of the
polyhedron (in % of avg_spacing)
                              avg distance points set <-> surface of the
polyhedron (in % of avg_spacing)]

        Poisson_reconstruction(points,   
                                        normals,                              
                             
                                        sm_angle,                             
      
                                        sm_radius,
                                        sm_distance,
                                        solver_name)
        \points python-list of Point_3
        \normals python-list returned by jet or pca_estimate_normals
        \sm_angle python-float Min triangle angle in degrees
        \sm_radius python-float triangle size w.r.t. pt set average spacing
        \sm_distance python-float Surface Approximation error w.r.t. point
set average spacing
        \solver_name "Eigen - built-in CG" or "Eigen - built-in simplicial
LDLt" for the compatibility of the 
         native solver in the machine, have to fixe this part...
        \return a Polyhedron_3

        Write_off_polyhedron(filename, Polyhedron)
        \filename the name of the .off file produice (have to pass
"my_file.off", with the .off)
        \Polyhedron the re-construction polyhedron
        \return None

Could something like this interest the cgal-binding repos? or 'How' could
something like this, finally interest the cgal-binding repos (with some
changes) ?

Then, I have two questions linked to my work, I want to produce an even
easier API for my work, something like:

my_polyhedron = CGAL.CGAL_Mesh_process_3.Poisson_reconstruction(points,
normals, nb_facets)

where nb_facets would be the final numbers of facets of the reconstructions
polyhedron. But I find it hard to predict the good sm_angle, sm_radius and
sm_distance for an appropriate nb facets... Could you give me some idea for
the issue?

Then, the second question is: how can we produce low-nb-facets polyhedron
with Poisson reconstruction?
I find it hard to produce 10_facets-polyhedrons or 50_facets-polyhedrons
with that algorithm...Could you give me some idea for this problem?

Thank Sebastien

Regards



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