Tunnels are a crucial component of national transportation infrastructure, playing a vital role in social development and urban planning. With the widespread application of 3D laser scanning technology in tunnel engineering, accurately extracting information from vast scanning data and filtering out noise points has become particularly important. To address this challenge, we proposed a Polar coordinate Cloth Simulation Filtering algorithm (P-CSF) to separate lining points from non-lining points in tunnel point cloud data, including tunnels of different shapes and different excavation methods. First, the dual projection method is used to obtain the approximate central axis of the tunnel point cloud. Secondly, a polar coordinate system is established with the roughly determined central axis of the tunnel as the pole, and the simulated cloth is constructed on the outermost part of the section. Subsequently, the gravitational model is used to shrink the cloth particles inward until the distance from the cloth particles to the measured point cloud is less than the specified threshold or the maximum number of iterations is reached. Finally, when the particle motion stops, the points that are in contact with the cloth particles are identified as lining points, while the points that are not in contact are considered as non-lining points. This algorithm was verified in a variety of tunnel scenarios, demonstrating its adaptability and effectiveness. Qualitative analysis indicates that the algorithm can adapt to various scenarios and can adjust the size of simulated cloth details to extract regions of interest as need. Quantitative analysis shows that the overall accuracy of the algorithm exceeded 90% in four typical scenarios, and each scenario obtained a kappa coefficient of nearly 80%, demonstrating its effective extraction capability. In the future, we will continue to optimize the algorithm to cope with more challenging scenarios.

中文翻译：

---

P-CSF：多类型隧道点云极坐标布模拟滤波算法


隧道是国家交通基础设施的重要组成部分，在社会发展和城市规划中发挥着至关重要的作用。随着 3D 激光扫描技术在隧道工程中的广泛应用，从大量扫描数据中准确提取信息并过滤掉噪声点变得尤为重要。为了应对这一挑战，我们提出了一种极坐标布料模拟过滤算法 （P-CSF），用于在隧道点云数据中分离衬砌点和非衬砌点，包括不同形状和不同开挖方法的隧道。首先，采用对偶投影法获取隧道点云的近似中心轴线。其次，以粗略确定的隧道中轴线为极点建立极坐标系，在断面最外侧构建模拟布料。随后，使用引力模型向内收缩布料颗粒，直到布料颗粒到测量点云的距离小于指定阈值或达到最大迭代次数。最后，当粒子运动停止时，与布料粒子接触的点被标识为衬线点，而未接触的点被视为非衬线点。该算法在各种隧道场景中进行了验证，证明了其适应性和有效性。定性分析表明，该算法可以适应各种场景，并可以根据需要调整模拟布料细节的大小以提取感兴趣的区域。 定量分析表明，该算法在四个典型场景中的整体准确率都超过了 90%，每个场景都获得了近 80% 的 kappa 系数，展示了其有效的提取能力。未来，我们将继续优化算法以应对更具挑战性的场景。