Cylinders extraction in non-oriented point clouds as a clustering problem. (November 2020) - Bergamasco, Filippo; Pistellato, Mara; Albarelli, Andrea; Torsello, Andrea
Highlights: Cylinder primitive extraction is addressed as a clustering problem of possible cylinder candidates. Candidates are generated by cutting the scene through several random planes. A novel cylinder similarity function based on dual quaternion algebra is defined. The clustering process, based on Game Theory, inherently discards incompatible candidates. The proposed method can deal with noisy, partially occluded and non-oriented point clouds. Abstract: Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed "in-the-wild", when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters.