In computer graphics and visualization, reconstruction of a 3D surface from a point cloud is an important research area.
As the surface contains information that can be measured, i.e. expressed in features, the application of surface
reconstruction can be potentially important for application in bio-imaging. Opportunities in this application area are the
motivation for this study. In the past decade, a number of algorithms for surface reconstruction have been proposed.
Generally speaking, these methods can be separated into two categories: i.e., explicit representation and implicit
Most of the aforementioned methods are firmly based in theory; however, so far, no analytical evaluation between these
methods has been presented. The straightforward way of evaluation has been by convincing through visual inspection.
Through evaluation we search for a method that can precisely preserve the surface characteristics and that is robust in the
presence of noise. The outcome will be used to improve reliability in surface reconstruction of biological models. We,
therefore, use an analytical approach by selecting features as surface descriptors and measure these features in varying
conditions. We selected surface distance, surface area and surface curvature as three major features to compare quality of
the surface created by the different algorithms. Our starting point has been ground truth values obtained from analytical
shapes such as the sphere and the ellipsoid.
In this paper we present four classical surface reconstruction methods from the two categories mentioned above, i.e. the
Power Crust, the Robust Cocone, the Fourier-based method and the Poisson reconstruction method. The results obtained
from our experiments indicate that Poisson reconstruction method performs the best in the presence of noise.