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dc.contributor.authorSreevalsan-Nair, Jayaen_US
dc.contributor.authorMohapatra, Pragyanen_US
dc.contributor.authorSingh, Satendraen_US
dc.contributor.editorFrosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanueleen_US
dc.date.accessioned2021-10-25T11:53:38Z
dc.date.available2021-10-25T11:53:38Z
dc.date.issued2021
dc.identifier.isbn978-3-03868-165-6
dc.identifier.issn2617-4855
dc.identifier.urihttps://doi.org/10.2312/stag.20211475
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20211475
dc.description.abstractBoth 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectAirborne LiDAR point clouds
dc.subjectLocal geometric descriptors
dc.subjectGlobal descriptor
dc.subjectClassification
dc.subjectBarycentric coordinates
dc.subjectVisualization
dc.subjectShannon entropy
dc.subjectDistribution distance measure
dc.subjectUncertainty analysis
dc.subjectCovariance tensor
dc.subjectTensor voting CCS Concepts
dc.subjectComputing methodologies
dc.subjectImage representations
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization techniques
dc.subjectInformation systems
dc.subjectNearest
dc.subjectneighbor search
dc.titleIMGD: Image-based Multiscale Global Descriptors of Airborne LiDAR Point Clouds Used for Comparative Analysisen_US
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.description.sectionheadersModeling, Reconstruction, and Applications
dc.identifier.doi10.2312/stag.20211475
dc.identifier.pages61-72


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