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dc.contributor.authorMetzer, Galen_US
dc.contributor.authorHanocka, Ranaen_US
dc.contributor.authorGiryes, Rajaen_US
dc.contributor.authorMitra, Niloy J.en_US
dc.contributor.authorCohen-Or, Danielen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2022-04-22T06:29:26Z
dc.date.available2022-04-22T06:29:26Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14487
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14487
dc.description.abstractWe present a technique for visualizing point clouds using a neural network. Our technique allows for an instant preview of any point cloud, and bypasses the notoriously difficult surface reconstruction problem or the need to estimate oriented normals for splat-based rendering. We cast the preview problem as a conditional image-to-image translation task, and design a neural network that translates point depth-map directly into an image, where the point cloud is visualized as though a surface was reconstructed from it. Furthermore, the resulting appearance of the visualized point cloud can be, optionally, conditioned on simple control variables (e.g., color and light). We demonstrate that our technique instantly produces plausible images, and can, on-the-fly effectively handle noise, non-uniform sampling, and thin surfaces sheets.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleZ2P: Instant Visualization of Point Cloudsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersVisualization
dc.description.volume41
dc.description.number2
dc.identifier.doi10.1111/cgf.14487
dc.identifier.pages461-471
dc.identifier.pages11 pages


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