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dc.contributor.authorLewiner, Thomasen_US
dc.contributor.authorCraizer, Marcosen_US
dc.contributor.authorLopes, Helioen_US
dc.contributor.authorPesco, Sinesioen_US
dc.contributor.authorVelho, Luizen_US
dc.contributor.authorMedeiros, Esdrasen_US
dc.date.accessioned2015-02-21T12:25:23Z
dc.date.available2015-02-21T12:25:23Z
dc.date.issued2006en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/j.1467-8659.2006.00990.xen_US
dc.description.abstractPerformances of actual mesh compression algorithms vary significantly depending on the type of model it encodes. These methods rely on prior assumptions on the mesh to be efficient, such as regular connectivity, simple topology and similarity between its elements. However, these priors are implicit in usual schemes, harming their suitability for specific models. In particular, connectivity-driven schemes are difficult to generalize to higher dimensions and to handle topological singularities. GEncode is a new single-rate, geometry-driven compression scheme where prior knowledge of the mesh is plugged into the coder in an explicit manner. It encodes meshes of arbitrary dimension without topological restrictions, but can incorporate topological properties, such as manifoldness, to improve the compression ratio. Prior knowledge of the geometry is taken as an input of the algorithm, represented by a function of the local geometry. This suits particularly well for scanned and remeshed models, where exact geometric priors are available. Compression results surfaces and volumes are competitive with existing schemes.en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleGEncode: Geometry-driven compression for General Meshesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume25en_US
dc.description.number4en_US
dc.identifier.doi10.1111/j.1467-8659.2006.00990.xen_US
dc.identifier.pages685-695en_US


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