  Astronomical observations, physical experiments as well as computer
simulations often involve discrete data sets supposed to represent a fair
sample of an underlying smooth and continuous field. Reconstructing the
underlying fields from a set of irregularly sampled data is therefore a
recurring key issue in operations on astronomical data sets. Conventional
methods involve artificial filtering through a grid or a smoothing kernel and
fail to achieve an optimal result. Here we describe a fully self-adaptive
geometric method which does not make use of artificial filtering, and which
makes optimal use of the available information.
