text: This paper builds upon the current methods to increase their capability and automation for 3D surface construction from noisy and potentially sparse point clouds. It presents an analysis of an artificial neural network surface regression and mapping method, describing caveats, improvements and justification for the different approach.
  @cite_8: The convex hull of a set of points is the smallest convex set that contains the points. This article presents a practical convex hull algorithm that combines the two-dimensional Quickhull algorithm with the general-dimension Beneath-Beyond Algorithm. It is similar to the randomized, incremental algorithms for convex hull and delaunay triangulation. We provide empirical evidence that the algorithm runs faster when the input contains nonextreme points and that it used less memory. computational geometry algorithms have traditionally assumed that input sets are well behaved. When an algorithm is implemented with floating-point arithmetic, this assumption can lead to serous errors. We briefly describe a solution to this problem when computing the convex hull in two, three, or four dimensions. The output is a set of “thick” facets that contain all possible exact convex hulls of the input. A variation is effective in five or more dimensions.
summary: An approach was suggested by @cite_8 using an image processing inspiration for surface de-noising. It removes noise by applying a Weiner filter which approximates the components of the surface as a statistical distribution. There are two problems with this algorithm in our context. First, it needs to be decided this algorithm should be applied, unnecessary smoothing might remove features that describe the underlying geometry, although there is some attempt to apply a surface based anisotropic diffusion to preserve edges. In addition, the formula used requires the user to both know and supply the noise denoted by the variance @math @cite_8 . It may not be possible to determine the noise of the data as it is an unstructured point cloud.