Section: Scientific Foundations
Keywords : Mesh processing, parameterization, texture mapping, fairing, splines.
Geometry Processing recently appeared (in the middle of the 90's) as a promising avenue to solve the geometric modeling problems encountered when manipulating meshes composed of million elements. Since a mesh may be considered to be a sampling of a surface - in other words a signal - the digital signal processing formalism was a natural theoretic background for this discipline (see e.g.  ). The discipline then studied different aspects of this formalism applied to geometric modeling.
Although many advances have been made in the Geometry Processing area, important problems still remain open. Even if shape acquisition and filtering is much easier than 30 years ago, a scanned mesh composed of 30 millions of triangles cannot be used directly in real-time visualization or complex numerical simulation. For this reason, automatic methods to convert those large meshes into higher level representations are necessary. However, these automatic methods do not exist yet. For instance, the pioneer Henri Gouraud often mentions in his talk that the data acquisition problem is still open. Malcolm Sabin, another pioneer of the ``Computer Aided Geometric Design'' and ``Subdivision'' approaches, mentioned during several conferences of the domain that constructing the optimum control-mesh of a subdivision surface so as to approximate a given surface is still an open problem. More generally, converting a mesh model into a higher level representation, consisting of a set of equations, is a difficult problem for which no satisfying solution have been proposed. This is one of the long-term goals of international initiatives, such as the AIMShape European network of excellence.
Motivated by gridding application for finite elements modeling for oil and gas exploration, in the frame of the Gocad project, we started studying Geometry Processing in the late 90's  and contributed to this area at the early stages of its development. We then developped new algorithms to add interactivity in the method  . To improve both the robustness and the flexibility of the method, we then studied a new algorithm to minimize the conformal energy, based on Cauchy-Riemann's equation. As a result, we developped the LSCM method (Least Squares Conformal Maps) in cooperation with Alias Wavefront  . We experimented various applications of the method, comprising normal mapping, mesh completion and fairing  , anisotropic remeshing  (in cooperation with P. Alliez and M. Desbrun) and light simulation  ,  .