Statistical Point Geometry

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dc.contributor.authorKalaiah, Aravinden_US
dc.contributor.authorVarshney, Amitabhen_US
dc.contributor.editorLeif Kobbelt and Peter Schroeder and Hugues Hoppeen_US
dc.date.accessioned2014-01-29T08:19:41Z
dc.date.available2014-01-29T08:19:41Z
dc.date.issued2003en_US
dc.description.abstractWe propose a scheme for modeling point sample geometry with statistical analysis. In our scheme we depart from the current schemes that deterministically represent the attributes of each point sample. We show how the statistical analysis of a densely sampled point model can be used to improve the geometry bandwidth bottleneck and to do randomized rendering without sacrificing visual realism. We first carry out a hierarchical principal component analysis (PCA) of the model. This stage partitions the model into compact local geometries by exploiting local coherence. Our scheme handles vertex coordinates, normals, and color. The input model is reconstructed and rendered using a probability distribution derived from the PCA analysis. We demonstrate the benefits of this approach in all stages of the graphics pipeline: (1) orders of magnitude improvement in the storage and transmission complexity of point geometry, (2) direct rendering from compressed data, and (3) view-dependent randomized rendering.en_US
dc.description.seriesinformationEurographics Symposium on Geometry Processingen_US
dc.identifier.isbn3-905673-06-1en_US
dc.identifier.issn1727-8384en_US
dc.identifier.urihttps://doi.org/10.2312/SGP/SGP03/107-115en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; G.3 [Mathematics of Computing]: Probability and Statisticsen_US
dc.titleStatistical Point Geometryen_US
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SGP03: Eurographics Symposium on Geometry Processing