论文速览 | IJRMMS：基于自适应聚类与曲面拟合的 3D 点云岩体不连续面产状自动提取

1. 传统手动测量（罗盘测绘、扫描线调查等）受可达性限制，依赖操作员经验，且存在安全风险，数据可重复性差；

2. 现有 3D 点云提取方法多为半自动化，需手动调整参数，或存在计算成本高、噪声鲁棒性差、分割精度不足的问题；

3. 边缘检测、区域生长等传统方法易受点云噪声、遮挡、采样密度不均影响，导致过分割或漏分割。

   岩体不连续面产状是工程设计与稳定性评估的关键参数，自动化、高精度、高效的提取方法能显著提升工程安全性与决策时效性，解决复杂地质场景下的测量难题。

该文提出的全自动化 3D 点云岩体不连续面产状提取方法，在精度与计算效率上实现双重突破，有效解决了传统方法的场景限制与效率瓶颈。

1. 为隧道开挖、边坡治理等危险或难以到达的场景提供安全、快速的结构面测量工具，降低现场作业风险；

2. 摆脱对人工参数调整和干预的依赖，提升复杂地质条件下（噪声、采样不均）的提取鲁棒性；

3. 为岩体力学数值模拟、地质灾害监测等领域提供高质量基础数据，推动工程地质分析的自动化与精准化发展。

原文摘要

The orientation of rock discontinuities is a critical parameter for evaluating the stability and safety of rock engineering structures. With the continuous advancement of remote surveying techniques, analysis of exposed rock surfaces based on 3D point cloud data has emerged as a mainstream approach, owing to its high data fidelity and rich geometric information. However, efficiently and accurately extracting geometric parameters of rock discontinuities from point clouds remains a significant challenge. To address this issue, this study proposes an efficient method for the automatic extraction of geometric features of rock discontinuities from 3D point clouds. First, a downsampling and smoothing preprocessing strategy is employed to significantly enhance computational efficiency while preserving essential geometric features. Concurrently, a local geometric adjustment normal estimation algorithm is introduced to generate high-precision normals while retaining sharp structural features. An improved Unsupervised K-means (UKM) clustering algorithm is subsequently proposed to planar segmentation of the point cloud, enabling the automatic identification and classification of discontinuity orientations. Finally, an enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is adopted to achieve precise planar segmentation, followed by Random Sample Consensus (RANSAC) method to ensure accurate extraction of discontinuity orientations. Experiments conducted on two real-world datasets demonstrate that the proposed method outperforms four widely used approaches in terms of both accuracy and computational efficiency, providing a novel and effective solution for the automated extraction of structural surface information in rock engineering applications.

译文

岩体不连续面产状是评估岩体工程结构稳定性与安全性的关键参数。随着遥感测量技术的持续发展，基于 3D 点云数据的裸露岩体表面分析因其数据保真度高、几何信息丰富，已成为主流研究手段。然而，从点云中高效精准地提取岩体不连续面几何参数仍是一项重大挑战。为解决该问题，本研究提出一种从 3D 点云自动提取岩体不连续面几何特征的高效方法。首先，采用下采样与平滑预处理策略，在保留核心几何特征的同时显著提升计算效率；同时，引入局部几何校正法向量估计算法，生成高精度法向量的同时保留尖锐结构特征。随后，提出改进的无监督 K 均值（UKM）聚类算法用于点云平面分割，实现不连续面方向的自动识别与分类。最后，采用增强型基于密度的噪声应用空间聚类（DBSCAN）算法实现精准平面分割，结合随机抽样一致性（RANSAC）方法确保不连续面产状的准确提取。在两个真实数据集上的实验表明，所提方法在精度与计算效率上均优于四种广泛使用的方法，为岩体工程中结构面信息的自动化提取提供了一种新颖有效的解决方案。