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Point clouds are the main output of automatic data collection using laser-scanner technologies. The large numbers of points scanned from pipeline plants make the plant reconstruct very difficult. We have developed a system based on the Point cloud Library (PCL), to find, recognize and reconstruct 3D pipes within point-clouds fully automatically. The proposed approach consists of pre-processing point cloud data, segmentation, skeleton extraction and automatic cylinder fitting for pipeline. The presented results shows that the proposed method enables reliable 3D models of pipelines, which could be successfully incorporated into the reconstruction of a plant information modelling method and utilized for, assist maintenance and expansion of existing plants.

Manual 3D pipeline modeling from LiDAR scanned point cloud data is laborious and time-consuming process. This paper presents a method to extract the pipe, elbow and branch information which is essential to the automatic modeling of the pipeline connection. The pipe geometry is estimated from the point cloud data through the Hough transform and the elbow position is calculated by the medial axis intersection for assembling the nearest pair of pipes. The branch is also created for a pair of pipe segments by estimating the virtual points on one pipe segment and checking for any feasible intersection with the other pipe's endpoint within the pre-defined range of distance. As a result of the automatic modeling, a complete 3D pipeline model is generated by connecting the extracted information of pipes, elbows and branches.