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Author Pintus, Ruggero ♦ Gobbetti, Enrico ♦ Callieri, Marco
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Photo blending ♦ Massive models ♦ Point clouds ♦ Streaming ♦ Texture blending
Abstract We 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 multiband 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.
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-11-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 4
Issue Number 2
Page Count 15
Starting Page 1
Ending Page 15

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Source: ACM Digital Library