![]() ![]() ![]() Novel Point Cloud Quality Improvement Method ![]() This recent study by the GeoSpatial Laboratory of the Faculty of Letters and Human Sciences at the Lebanese University applied various methods for point cloud quality enhancement and also conducted field experiments to reduce the undesirable irregularities of point clouds. Point cloud datasets usually contain a considerable number of undesirable irregularities, such as strong variability of local point density, missing data, overlapping points and noise. The accuracy of datasets generated from drone flights depends on the data capturing methods, from active Lidar sensors or from passive remote-sensing sensors (cameras). ![]() Unmanned aerial vehicles (UAVs or ‘drones’) have become increasingly popular for many environmental applications, delivering point cloud digital surface models (DSMs) and orthoimagery. What if were possible to merge these technologies? What effect might neutral density filters have on point cloud colours? This article explores quantitative and qualitative point cloud enhancement in more detail. Passive imaging cameras derive a more detailed 3D model and encode point clouds with multispectral information, resulting in a useful coloured point cloud classification. Is it possible to enhance point cloud accuracy and density by merging Lidar with photogrammetry technologies? Lidar data can penetrate trees and measure shadow areas to produce a very accurate point cloud. ![]()
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