Digital-Discrete Surface Reconstruction: A true universal and nonlinear method
For researchers in data reconstruction, this work offers a methodological perspective on a nonlinear alternative to linear methods, but it is primarily conceptual without concrete results.
The paper presents a digital-discrete method for surface reconstruction that is claimed to be truly universal and nonlinear, unlike popular linear methods. It explains the problem and why this approach is necessary.
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.