Reconstruction of Implicit Surfaces from Fluid Particles Using Convolutional Neural Networks

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dc.contributor.authorZhao, Chenen_US
dc.contributor.authorShinar, Tamaren_US
dc.contributor.authorSchroeder, Craigen_US
dc.contributor.editorSkouras, Melinaen_US
dc.contributor.editorWang, Heen_US
dc.date.accessioned2024-08-20T08:43:20Z
dc.date.available2024-08-20T08:43:20Z
dc.date.issued2024
dc.description.abstractIn this paper, we present a novel network-based approach for reconstructing signed distance functions from fluid particles. The method uses a weighting kernel to transfer particles to a regular grid, which forms the input to a convolutional neural network. We propose a regression-based regularization to reduce surface noise without penalizing high-curvature features. The reconstruction exhibits improved spatial surface smoothness and temporal coherence compared with existing state of the art surface reconstruction methods. The method is insensitive to particle sampling density and robustly handles thin features, isolated particles, and sharp edges.en_US
dc.description.number8
dc.description.sectionheadersPhysics I: Fluids, Shells, and Natural Phenomena
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15181
dc.identifier.issn1467-8659
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15181
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15181
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies->Physical simulation; Point-based models
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.subjectPoint
dc.subjectbased models
dc.titleReconstruction of Implicit Surfaces from Fluid Particles Using Convolutional Neural Networksen_US
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