Sreevalsan-Nair, JayaMohapatra, PragyanSingh, SatendraFrosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele2021-10-252021-10-252021978-3-03868-165-62617-4855https://doi.org/10.2312/stag.20211475https://diglib.eg.org:443/handle/10.2312/stag20211475Both geometric and semantic information are required for a complete understanding of regions acquired as three-dimensional (3D) point clouds using the Light Detection and Ranging (LiDAR) technology. However, the global descriptors of such datasets that integrate both the information types are rare. With a focus on airborne LiDAR point clouds, we propose a novel global descriptor that transforms the point cloud from Cartesian to barycentric coordinate spaces. We use both the probabilistic geometric classification, aggregated from multiple scales, and the semantic classification to construct our descriptor using point rendering. Thus, we get an image-based multiscale global descriptor, IMGD. To demonstrate its usability, we propose the use of distribution distance measures between the descriptors for comparing the point clouds. Our experimental results demonstrate the effectiveness of our descriptor, when constructed of publicly available datasets, and on applying our selected distance measures.Airborne LiDAR point cloudsLocal geometric descriptorsGlobal descriptorClassificationBarycentric coordinatesVisualizationShannon entropyDistribution distance measureUncertainty analysisCovariance tensorTensor voting CCS ConceptsComputing methodologiesImage representationsHumancentered computingVisualization techniquesInformation systemsNearestneighbor search10.2312/stag.2021147561-72