Particle swarm optimization for non-uniform rational B-spline surface reconstruction from clouds of 3D data points
This work investigates the use of particle swarm optimization (PSO) to recover the shape of a surface from clouds of (either organized or scattered) noisy 3D data points, a challenging problem that appears recurrently in a wide range of applications such as CAD design, data visualization, virtual reality, medical imaging and movie industries. In this paper, we apply a PSO approach in order to reconstruct a non-uniform rational B-spline (NURBS) surface of a certain order from a given set of 3D data points. In this case, surface reconstruction consists of two main tasks: (1) surface parameterization and (2) surface fitting. Both tasks are critical but also troublesome, leading to a high-dimensional non-linear optimization problem. Our method allows us to obtain all relevant surface data (i.e., parametric values of data points, knot vectors, control points and their weights) in a shot and no pre-/post-processing is required. Furthermore, it yields very good results even in presence of problematic features, such as multi-branches, high-genus or self-intersections. Seven examples including open, semiclosed, closed, zero-genus, high-genus surfaces and real-world scanned objects, described in free-form, parametric and implicit forms illustrate the good performance of our approach and its superiority over previous approaches in terms of accuracy and generality.
particle swarm optimization surface reconstruction surface parameterization surface fitting reverse engineering nurbs surface