Point-Cloud-Based 3D Inspection and Volume Quantification of Drainage-Pipeline Defects Using WCPC-GAN and Density-Adaptive Alpha Shapes

Abstract

Closed-circuit television (CCTV)-based inspection provides limited depth information and cannot directly quantify the three-dimensional geometry of drainage-pipeline defects. Moreover, the scarcity of annotated point-cloud data and the topological artifacts produced by conventional surface-reconstruction methods hinder automated condition assessment. This study presents a point-cloud-based framework for 3D inspection and volume quantification of concrete drainage-pipeline defects. WCPC-GAN expands the available defect data; PointNeXt segments the point clouds; and a RANSAC-constrained, density-adaptive Alpha Shape method reconstructs the defect surface for volume integration. The framework was evaluated using 15 independently fabricated circular, triangular, and rectangular defects, with reference volumes obtained through repeated water-displacement measurements. The proposed reconstruction achieved mean volume accuracies of 96.32 ± 0.67%, 96.78 ± 0.60%, and 96.86 ± 0.37%, respectively, and exceeded a contemporary CAP-UDF baseline by 1.62, 2.68, and 3.06 percentage points. Paired-bootstrap analysis estimated an overall error reduction of 5.56 percentage points over conventional Alpha Shape (95% confidence interval: 5.17–5.97). Synthetic augmentation also increased PointNeXt performance on the fixed real-only test set from 92.52% accuracy and 83.53% mIoU to 94.53% and 89.98%, respectively. The results support physically calibrated defect-volume assessment under controlled experimental conditions.