This study aims to address the challenge of incomplete point clouds acquired by underground LiDAR sensors under dust, water mist and occlusion conditions. This study focuses on achieving high-fidelity 3D measurement and completion of coal-mine roadways to ensure sensing accuracy and measurement reliability for intelligent mining.
The authors propose PF-Net++, an enhanced point fractal network integrating a combined structure-aware point transformer (CSA-PT) encoder with a point-pyramid decoder using multiscale skip connections. The CSA-PT leverages the roadway centerline as a structural prior to guide anisotropic neighborhood construction. In addition, the authors construct a real-world benchmark, termed the roadway point cloud patch data set (RPCD), for model evaluation.
Experiments on the RPCD show that PF-Net++ achieves the best overall performance among the compared baselines across most evaluation metrics. Compared to SymmCompletion, it achieves a 32.3% reduction in Chamfer distance (CD) (GT→Pred), a 6.3% gain in the strictest F1-score (d = 0.01), and the lowest CD (Pred→GT) of 0.3508. Evaluations on Point Completion Network and multi-view partial benchmarks further demonstrate competitive generalization beyond roadway scenes. Ablation studies further confirm the complementarity of the proposed components, yielding a 25.2% improvement in F1-score and a 19.8% reduction in CD compared with single-component variants.
This research integrates a roadway centerline structural prior into a CSA-PT to handle weakly textured environments and contributes the RPCD benchmark. The proposed framework enables high-fidelity completion of roadway point clouds, thereby improving data quality for underground digital mapping and safety assessment.
