A Streaming Framework for Seamless Detailed Photo Blending on Massive Point Clouds

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dc.contributor.authorPintus, Ruggeroen_US
dc.contributor.authorGobbetti, Enricoen_US
dc.contributor.authorCallieri, Marcoen_US
dc.contributor.editorA. Day and R. Mantiuk and E. Reinhard and R. Scopignoen_US
dc.date.accessioned2014-02-06T15:10:41Z
dc.date.available2014-02-06T15:10:41Z
dc.date.issued2011en_US
dc.description.abstractWe present an efficient scalable streaming technique for mapping highly detailed color information on extremely dense point clouds. Our method does not require meshing or extensive processing of the input model, works on a coarsely spatially-reordered point stream and can adaptively refine point cloud geometry on the basis of image content. Seamless multi-band image blending is obtained by using GPU accelerated screen-space operators, which solve point set visibility, compute a per-pixel view-dependent weight and ensure a smooth weighting function over each input image. The proposed approach works independently on each image in a memory coherent manner, and can be easily extended to include further image quality estimators. The effectiveness of the method is demonstrated on a series of massive real-world point datasets.en_US
dc.description.seriesinformationEurographics 2011 - Areas Papersen_US
dc.identifier.issn1017-4656en_US
dc.identifier.urihttps://doi.org/10.2312/EG2011/areas/025-032en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): Computer Graphics [I.3.3]: Digitizing and scanning- ; Computer Graphics [I.3.7]: Three-Dimensional Graphics and Realismen_US
dc.titleA Streaming Framework for Seamless Detailed Photo Blending on Massive Point Cloudsen_US
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