  Writing an uncomplicated, robust, and scalable three-dimensional convex hull
algorithm is challenging and problematic. This includes, coplanar and collinear
issues, numerical accuracy, performance, and complexity trade-offs. While there
are a number of methods available for finding the convex hull based on
geometric calculations, such as, the distance between points, but do not
address the technical challenges when implementing a usable solution (e.g.,
numerical issues and degenerate cloud points). We explain some common algorithm
pitfalls and engineering modifications to overcome and solve these limitations.
We present a novel iterative method using support mapping and surface
projection to create an uncomplicated and robust 2d and 3d convex hull
algorithm.
