Rational Supershapes for Surface Reconstruction

Simple representation of complex 3D data sets is a fundamental problem in computer vision. From a quality control perspective, it is crucial to use efficient and simple techniques do define a reference model for further recognition or comparison tasks. In this paper, we focus on reverse engineering 3D data sets by recovering rational supershapes to build an implicit function to represent mechanical parts. We derive existing techniques for superquadrics recovery to the supershapes and we adapt the concepts introduced for the ratioquadrics to introduce the rational supershapes. The main advantage of rational supershapes over standard supershapes is that the radius is now expressed as a rational fraction instead of sums and compositions of powers of sines and cosines, which allows simpler and faster computations during the optimization process. We present reconstruction results of complex 3D data sets that are represented by an implicit equation with a small number of parameters that can be used to build an error measure.