An adaptive approach for the reconstruction and modeling of as-built 3D pipelines from point clouds

Automated extraction of 3D geometric shapes such as planes, spheres, cylinders, cones, and tori in laser-scanned point clouds is a challenging problem and a tedious process, especially when using cluttered data. This paper describes a modification of the existing Hough transform for the automatic detection of cylinder parameters in point clouds. Careful analysis reveals that the existing method still has excessive space and time complexity or yields imprecise outcomes. The approach described here modifies the orientation estimation with an area-based adaptive method that utilizes a small accumulator to detect significant peaks in the Hough space in the presence of single or multiple cylinders in the point cloud data. After orientation estimation, the position and radius are estimated using an orthonormal coordinate system with a circle fitting algorithm. These modifications are tested with extensive sets of real point cloud data, and experimental results show that the presented approach minimizes the space and time complexity. After detection, the relationship between cylinders is reconstructed to form a continuous axis network by tracking cylinder parameters obtained from earlier steps. Using the axis network of cylinders obtained from point clouds, models of entire pipelines that include straight pipes, elbow joints, and T-junctions are determinately defined, and output data is reconstructed in Smart Plant 3D (SP3D). The presented results show that the proposed approach indeed improves the computational complexity by reducing the space and time, and yields methods that can be employed in the automation of 3D pipeline model reconstruction.