Curvature Weighted Decimation: A Novel, Curvature-Based Approach to Improved Lidar Point Decimation of Terrain Surfaces

Increased availability of QL1/QL2 Lidar terrain data has resulted in large datasets, often including quantities redundant points. Because these memory requirements, practitioners use decimation to reduce the number points used create models. This paper introduces a novel approach improve decimation, thereby reducing total count ground dataset while retaining more accuracy than Random Decimation. reduction improves efficiency downstream processes maintaining output quality nearer undecimated dataset. Points are selected for retention based on their discrete curvature values computed from mesh geometry TIN model with higher preferred resulting point cloud. We call this technique Curvature Weighted Decimation (CWD). implement CWD new free, open-source software tool, CogoDN, which is also introduced paper. evaluate effectiveness against by comparing error two kinds over multiple percentages, statistical types, and types. The results show that reduces when 15 50% retained.