Kernel-Based Sampling of Arbitrary Data

dc.contributor.authorCammarasana, Simoneen_US
dc.contributor.authorPatanè, Giuseppeen_US
dc.contributor.editorBiasotti, Silvia and Pintus, Ruggero and Berretti, Stefanoen_US
dc.date.accessioned2020-11-12T05:42:10Z
dc.date.available2020-11-12T05:42:10Z
dc.date.issued2020
dc.description.abstractPoint sampling is widely used in several Computer Graphics' applications, such as point-based modelling and rendering, image and geometric processing. Starting from the kernel-based sampling of signals defined on a regular grid, which generates adaptive distributions of samples with blue-noise property, we specialise this sampling to arbitrary data in terms of dimension and structure, such as signals, vector fields, curves, and surfaces. To demonstrate the novelties and benefits of the proposed approach, we discuss its applications to the resampling of 2D/3D domains according to the distribution of physical quantities computed as solutions to PDEs, and to the sampling of vector fields, 2D curves and 3D point sets. According to our experiments, the proposed sampling achieves a high approximation accuracy, preserves the features of the input data, and is computationally efficient.en_US
dc.description.sectionheadersSampling and Rendering
dc.description.seriesinformationSmart Tools and Apps for Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20201252
dc.identifier.isbn978-3-03868-124-3
dc.identifier.issn2617-4855
dc.identifier.pages171-180
dc.identifier.urihttps://doi.org/10.2312/stag.20201252
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20201252
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectPoint
dc.subjectbased models
dc.subjectMesh models
dc.subjectImage processing
dc.titleKernel-Based Sampling of Arbitrary Dataen_US
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