将岩体结构与锚杆信息融合，实现地下矿井三维点云中的集成支护可视化。
Towards Integrated Rock Support Visualisation in 3D Point Cloud of Underground Mines
- 整合结构映射、锚杆识别与平面拟合，统一处理点云数据。
- 准确识别中等规模点云中的断层面和锚杆，生成几何关系可视化结果。
- 适合地质工程评估人员使用，可替代人工测量提升效率。
地下矿井中岩体支护的效能取决于安装锚杆与围岩结构特征之间的相互作用。然而，断层特征刻画与锚杆识别通常被作为独立任务处理，限制了其在综合支护评估中的应用价值。本研究提出一种基于地下矿洞三维点云的自动化集成支护可视化框架，将结构映射、锚杆识别、断层面拟合及锚杆方向估计整合为统一工作流，兼顾精度与计算效率。输出结果用于生成拟合断层面与锚杆矢量的三维集成视图，可直接评估其空间交集与几何关系。同时进行断层极点与锚杆方向的立体投影分析，以评估锚杆布置相对于结构特征的整体几何有效性。此外，还可视化了锚杆级质量指标，包括外露长度和偏离局部顶板法线的程度，支持安装质量评估。该框架在真实地下金属矿山扫描数据上验证，可在中等规模点云中实现高精度的结构映射与锚杆识别。研究为无需人工测量或额外现场数据采集的自动化、集成化岩体支护效能评估提供了实用路径。
原文摘要 · Abstract (English)
The effectiveness of rock support in underground mines depends on the interaction between installed rock bolts and the structural fabric of the surrounding rock mass. However, discontinuity characterisation and rock bolt identification are commonly treated as separate tasks, limiting their value for integrated support assessment. This study presents an automated framework for integrated rock support visualisation using 3D point clouds of underground mine excavations. The framework integrates structure mapping, rock bolt identification, discontinuity plane fitting, and bolt orientation estimation into a unified workflow optimised for accuracy and computational efficiency. The outputs are used to generate an integrated 3D visualisation of fitted discontinuity planes and bolt vectors, enabling direct assessment of their spatial intersections and geometric relationships. A complementary stereographic analysis of discontinuity poles and bolt orientations is also performed to evaluate overall bolting geometric effectiveness relative to the mapped structural fabric. Additionally, bolt-level quality metrics, including exposed protrusion length and deviation from the local roof normal, are visualised to support assessment of installation quality. The proposed framework is demonstrated on real underground metal mine scans, producing accurate structure mapping and rock bolt identification results in medium-scale point clouds. Overall, the study provides a practical step towards automated, integrated geotechnical assessment of rock support effectiveness without requiring manual measurements or additional in-situ data acquisition.
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