Given a finite sampling $P\subset\mathbbR^d$ of an unknown surface $S$, surface reconstruction is concerned with the calculation of a model of $S$ from $P$. The model can be represented as a smooth or a triangulated surface, and is expected to match $S$ from a topological and geometric standpoints. In this survey, we focus on the recent developments of Delaunay based surface reconstruction methods, which were the first methods (and in a sense still the only ones) for which one can precisely state properties of the reconstructed surface. We outline the foundations of these methods from a geometric and algorithmic standpoints. In particular, a careful presentation of the hypothesis used by these algorithms sheds light on the intrinsic difficulties of the surface reconstruction problem faced by any method, Delaunay based or not.
