Surface Reconstruction with Enriched Reproducing Kernel Particle Approximation

Abstract

There are many techniques that reconstruct continuous 3D surfaces from scattered point data coming from laser range scanners. One of the most commonly used representations are Point Set Surfaces (PSS) defined as the set of stationary points of a Moving Least Squares (MLS) projection operator. One interesting property of the MLS projection is to automatically filter out high frequency noise, that is usually present in raw data due to scanning errors. Unfortunately, the MLS projection also smoothes out any high frequency feature, such as creases or corners, that may be present in the scanned geometry, and does not offer any possibility to distinguish between such feature and noise. The main contribution of this paper, is to present an alternative projection operator for surface reconstruction, based on the Enriched Reproducing Kernel Particle Approximation (ERKPA), which allows the reconstruction process to account for high frequency features, by letting the user explicitly tag the corresponding areas of the scanned geometry.