ESphere: extracting spheres from unorganized point clouds

Abstract

Spheres are popular geometric primitives found in many manufactured objects. However, sphere fitting and extraction have not been investigated in depth. In this paper, a robust method is proposed to extract multiple spheres accurately and simultaneously from unorganized point clouds. Moreover, a novel validation step is presented to assess the quality of the detected spheres, which help remove the confusion between perfect spheres and sphere-like shapes such as ellipsoids and paraboloids. A novel sampling strategy is introduced to reduce computational burden for sphere extraction. Experiments on both synthetic and scanned point clouds with different levels of noise and outliers are conducted and the results compared to state-of-the-art methods. These experiments demonstrate the efficiency and robustness of the proposed sphere extraction method.