Detecting undersampling in surface reconstruction: Theory and Experiments

satisfactorily on well-sampled, smooth surfaces without
boundaries. However, these algorithms have severe problems
when met with undersampling. Cases of undersampling
is prevalant in real data since often they sample a part
of the boundary of an object, or are derived from
a nonsmooth surface. In this paper we present an algorithm
to detect the regions of undersampling. This information can
be used to reconstruct surfaces with boundaries, and also to
detect the locality of sharp features in nonsmooth surfaces.
We report the effectiveness of the algorithm with a number
of experimental results. Theoretically, we justify the
algorithm with some mild assumptions that are valid for most
practical data.


Note: This is a joint work with Joachim Giesen.