Semi-Automated Segmentation of Geometric Shapes from Point Clouds

Abstract

Building information models (BIM) in the civil industry are very popular nowadays. The basic information of these models is the 3D geometric model of a building structure. The most applied methodology to model the existing buildings is by generating 3D geometric information from point clouds provided by laser scanners. The fundamental principle of this methodology is the recognition of structures shaped in basic geometric primitives, e.g., planes, spheres, and cylinders. The basic premise of the efficiency of this methodology is the automation of detection, since manual segmentation of a point cloud can be challenging, time-consuming, and, therefore, inefficient. This paper presents a novel algorithm for the automated segmentation of geometric shapes in point clouds without needing pre-segmentation. With the designed algorithm, structures formed in three types of basic geometrical primitive can be identified and segmented: planar (e.g., walls, floors, ceilings), spherical (e.g., laser scanner reference targets), and cylindrical (e.g., columns, pillars, piping). The RANSAC paradigm partially inspires the proposed algorithm; however, various modifications must be made. The algorithm was tested on several point clouds and was compared with the standard RANSAC algorithm; this part is described in the last section of the paper. One of the tests was performed on a double cylinder-shaped test object, the parameters (radius and height) of this object were available with high accuracy (0.1 mm), and the differences between the known and estimated parameters were below 0.5 mm in each case, indicating the correctness of the proposed algorithm. Also, a comparison with the standard RANSAC algorithm was performed, where the algorithm proposed showed better results than the standard RANSAC algorithm. The segmentation quality was, on average, increased from 50% to 100%.