![]() ![]() The problem we address with Curvature Weighted Decimation (CWD) is a type of data compression algorithm using geometric characteristics of the data to prioritize points for retention or elimination. The new Curvature Weighted Decimation algorithm, introduced here, is designed to reduce the number of points in a Lidar terrain point cloud dataset while minimizing the elevation accuracy loss of the reduced dataset. For example, they pointed out that a Geiger-mode scanner returns 25 pulses per square meter, and this high density increases storage requirements and processing time. noted that there are advantages to decimating point clouds in some applications. Recent increases in Lidar point density have increased the need to filter (or decimate) these large datasets to reduce the memory footprint while retaining acceptable levels of accuracy. The results show that CWD reduces introduced error values over Random Decimation when 15 to 50% of the points are retained. We evaluate the effectiveness of CWD against Random Decimation by comparing the resulting introduced error values for the two kinds of decimation over multiple decimation percentages, multiple statistical types, and multiple terrain types. We implement CWD in a new free, open-source software tool, CogoDN, which is also introduced in this paper. We call this technique Curvature Weighted Decimation (CWD). Points with higher curvature values are preferred for retention in the resulting point cloud. Points are selected for retention based on their discrete curvature values computed from the mesh geometry of the TIN model of the points. This reduction improves efficiency of downstream processes while maintaining output quality nearer to the undecimated dataset. This paper introduces a novel approach to improve decimation, thereby reducing the total count of ground points in a Lidar dataset while retaining more accuracy than Random Decimation. Because of these large memory requirements, practitioners often use decimation to reduce the number of points used to create models. Increased availability of QL1/QL2 Lidar terrain data has resulted in large datasets, often including large quantities of redundant points. ![]()
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