Semantic-to-instance top-down analysis of complex industrial pipelines in 3D point cloud scenarios

Pipelines are common and critical components in some complex industrial equipment, characterized by intricate bending patterns and hierarchical distributions across irregular surfaces. Segmenting pipeline instances from the background for further inspection is a challenging task. To address this challenge, we propose a comprehensive top-down framework that follows a semantic-to-instance integrated task flow for pipeline analysis. We first design a deep neural network model that utilizes both positional embedding and pipeline-aware geometric features to distinguish bent and long pipeline point clouds from the background. Then, we introduce an enhanced axis-growing algorithm that incorporates robustness optimization strategies to achieve precise instance-level axis reconstruction and recover key geometric parameters. Furthermore, we develop a simple yet effective model for accurate minimum clearance measurement between adjacent pipelines. The experimental results on real-world point cloud datasets demonstrate the effectiveness of the proposed framework. The pipeline segmentation achieves a superior accuracy of 95.89%. The minimum clearance measurement reaches mean accuracies of 0.028 mm for straight pipelines and 0.08 mm for bent pipelines, respectively.