Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing
This work addresses the challenge of 3D shape fitting for photogrammetry applications, but it is incremental as it focuses on cylinder approximations with specific operators.
The paper tackles the problem of reconstructing 3D shapes from noisy and incomplete point clouds by proposing an evolutionary optimization method that approximates geometry using primitive shapes, specifically cylinders, and demonstrates its applicability in synthetic and real-life cases like vegetation and industrial settings.
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.