Title: Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing,
Abstract: Point clouds obtained from photogrammetry are noisy and incomplete models of
reality. We propose an evolutionary optimization methodology that is able to
approximate the underlying object geometry on such point clouds. This approach
assumes a priori knowledge on the 3D structure modeled and enables the
identification of a collection of primitive shapes approximating the scene.
Built-in mechanisms that enforce high shape diversity and adaptive population
size make this method suitable to modeling both simple and complex scenes. We
focus here on the case of cylinder approximations and we describe, test, and
compare a set of mutation operators designed for optimal exploration of their
search space. We assess the robustness and limitations of this algorithm
through a series of synthetic examples, and we finally demonstrate its general
applicability on two real-life cases in vegetation and industrial settings.