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dc.contributor.authorRoveri, Riccardoen_US
dc.contributor.authorÖztireli, A. Cengizen_US
dc.contributor.authorPandele, Ioanaen_US
dc.contributor.authorGross, Markusen_US
dc.contributor.editorGutierrez, Diego and Sheffer, Allaen_US
dc.date.accessioned2018-04-14T18:23:07Z
dc.date.available2018-04-14T18:23:07Z
dc.date.issued2018
dc.identifier.issn1467-8659
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.13344
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13344
dc.description.abstractWith the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of assumptions such as local smoothness to tackle this ill-posed problem. However, such priors lead to loss of important features and geometric detail. Instead, we propose a novel data-driven approach for point cloud consolidation via a convolutional neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons to the state-of-the-art.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectPoint
dc.subjectbased models
dc.titlePointProNets: Consolidation of Point Clouds with Convolutional Neural Networksen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersGeometry Learning
dc.description.volume37
dc.description.number2
dc.identifier.doi10.1111/cgf.13344
dc.identifier.pages87-99


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