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Point clouds obtained from photogrammetry are noisy and incomplete models ofreality. We propose an evolutionary optimization methodology that is able toapproximate the underlying object geometry on such point clouds. This approachassumes a priori knowledge on the 3D structure modeled and enables theidentification of a collection of primitive shapes approximating the scene.Built-in mechanisms that enforce high shape diversity and adaptive populationsize make this method suitable to modeling both simple and complex scenes. Wefocus here on the case of cylinder approximations and we describe, test, andcompare a set of mutation operators designed for optimal exploration of theirsearch space. We assess the robustness and limitations of this algorithmthrough a series of synthetic examples, and we finally demonstrate its generalapplicability on two real-life cases in vegetation and industrial settings.

Fitting 3D Shapes from Partial and Noisy Point Clouds with Evolutionary Computing