Multiscale Mesh Fitting Filtering Based on Adaptive Clustering Segmentation and Gradient Compensation

Point cloud filtering serves as a core step in point cloud data processing, which is critical for constructing high-precision digital elevation models and conducting terrain monitoring. However, existing filtering methods encounter two key challenges. First, they exhibit strong reliance on threshold parameters and limited adaptability, necessitating manual parameter adjustment to suit different scenarios—this often results in misclassification or under-classification of ground and nonground points. Second, their adaptability to complex terrains is inadequate: in regions with drastic slope variations or dense buildings, issues such as the loss of terrain details or confusion between terrain points and feature point clouds frequently arise, making it difficult for a single method to meet the requirements of diverse application scenarios. To address this problem, this article proposes a multiscale mesh fitting filtering based on adaptive clustering segmentation and gradient compensation. First, the existing clustering segmentation algorithm is improved to propose an adaptive clustering segmentation method. Then, by combining Delaunay triangulation and local feature parameters, an outlier cluster is constructed. On the basis of the clustering segmentation results and the distribution of outliers, the building feature areas are effectively separated. After that, grids are constructed by separating the point clouds of the building clusters. The point clouds within the grids are judged by using planes and slope angles to remove most nonground points and establish a secondary grid. The elevation threshold is determined on the basis of the fitted grids and gradient compensation. Finally, carrying out a secondary check according to the cluster edges obtained by clustering. The experimental results demonstrate that the proposed method has wide applicability without the need to switch feature parameters, and its filtering accuracy is significantly higher than that of existing methods in most point cloud scenarios.