Transmission line safety is vital to the nation’s economy and daily lives. In recent decades, most electric utilities have relied on light detection and ranging (LiDAR) technology to inspect transmission line corridors. However, occlusion and point density gaps in scanned LiDAR data will affect power line extraction. Moreover, high-voltage transmission line (HVTL) corridors in complicated locations with multiple loops pose significant challenges. The detailed modeling of power lines is a prerequisite for detecting potential risks. Thus, this study introduces a fourfold strategy to extract and correctly reconstruct huge gaps in HVTL conductor bundles. In the first step, the power lines are retrieved as span points using the 3-D voxel grid. The second step generates bundle masks by splitting span points into several segments. A bipartite graph connects bundle segments with wide gaps utilizing bundle location information from these masks. Third, bundles are segmented again to form conductor masks for sub-conductor extraction utilizing image-based algorithms and probability. Finally, the mathematical model reconstructs the retrieved power lines as sub-conductors. The proposed technique is tested on 58 spans with different power line configurations from two low-point-density datasets with a few constant parameters. The test results demonstrate the resilience of the proposed method in effectively repairing large gaps in bundles and sub-conductors. The sub-conductors are retrieved with 90% accuracy. The suggested method robustly reconstructs sub-conductors with a fitting residual error of less than 0.07 m.