  The most common problem in data reconstruction is to fit a function based on
the observations of some sample (guiding) points. This paper provides a
methodological point of view of digital-discrete surface reconstruction. We
explain our method along with why it is a truly universal and nonlinear method
unlike most popular methods, which are linear and restricted. This paper
focuses on what the surface reconstruction problem is and why the
digital-discrete method is important, necessary, and how it can be
accomplished.
