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

作者：Zitao Lin, Guoliang Chen, Chuanli Kang, Zhenghua Zhang, Hu Liu · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2025 · DOI：10.1109/jstars.2025.3645779 · 被引用次数：1 · 研究领域：Remote Sensing and LiDAR Applications、3D Shape Modeling and Analysis、3D Surveying and Cultural Heritage

Point cloud filtering serves as a core step in point cloud data processing, which is critical for constructing high-precision Digital Elevation Models (DEMs) 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 non-ground 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 multi-scale 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 grid...