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More precisions about reconstruction methods principles

This commit is contained in:
Simon Giraudot 2016-01-08 09:03:56 +01:00
parent 54bf98bf02
commit 93038ff7e9
1 changed files with 19 additions and 8 deletions
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27
Documentation/doc/Documentation/Tutorials/Tutorial_reconstruction.txt
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@ -251,10 +251,14 @@ consistent.
\subsection TutorialsReconstruction_reconstruction_poisson Poisson
Poisson reconstruction uses points with normals to produce smooth
closed surfaces. It is not indicated if the surface is expected to
pass exactly on the input points. On the contrary, it performs well if
the aim is to approximate a noisy point cloud with a smooth surface.
Poisson reconstruction consists in computing an indicator function
whose gradient matches the input normal vector field: this indicator
function has opposite signs inside and outside of the inferred shape
(hence the need for closed shapes). This method thus requires normals
and produces smooth closed surfaces. It is not appropriate if the
surface is expected to pass exactly on the input points. On the
contrary, it performs well if the aim is to approximate a noisy point
cloud with a smooth surface.
Notice that it does not generate directly a mesh but computes an
_implicit function_ (that can later be used to generate a mesh):
@ -308,7 +312,10 @@ be used:
Advancing front is a Delaunay-based approach that generates triples of
point indices that describe the triangular facets of the
reconstruction. Its main asset is to generate oriented manifold
reconstruction: it uses a priority queue to sequentially pick the
Delaunay facet the most likely to be part of the surface, based on a
size criterion (to favor the small facets) and an angle criterion (to
favor smoothness). Its main asset is to generate oriented manifold
surfaces with boundaries: contrary to Poisson, it does not require
normals and is not bound to reconstruct closed shapes. However, it
requires preprocessing if the point is noisy.
@ -329,9 +336,13 @@ CGAL::advancing_front_surface_reconstruction(points.begin(),
Scale space reconstruction aims at producing a surface that
interpolates the input points (interpolant) while offering some
robustness to noise. It is the good choice if the input point cloud is
noisy but the user still wants the surface to pass exactly through the
points.
robustness to noise. More specifically, it first applies several times
a smoothing filter to the input point set to produce a scale space;
then, the smoothest scale is meshed using an alpha shape; finally, the
resulting connectivity between smoothed points is propagated to the
original raw input point set. This method is the right choice if the
input point cloud is noisy but the user still wants the surface to
pass exactly through the points.
Notice that although there is an option to force the output to be
manifold, it is not guaranteed to be orientable (contrary to _Poisson_