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dc.contributor.authorFei, Guangzhengen_US
dc.contributor.authorCai, Kangyingen_US
dc.contributor.authorGuo, Bainingen_US
dc.contributor.authorWu, Enhuaen_US
dc.date.accessioned2015-02-16T07:23:19Z
dc.date.available2015-02-16T07:23:19Z
dc.date.issued2002en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/1467-8659.00571en_US
dc.description.abstractCurrent out-of-core simplification algorithms can efficiently simplify large models that are too complex to be loaded in to the main memory at one time. However, these algorithms do not preserve surface details well since adaptive sampling, a typical strategy for detail preservation, remains to be an open issue for out-of-core simplification. In this paper, we present an adaptive sampling scheme, called the balanced retriangulation (BR), for out-of-core simplification. A key idea behind BR is that we can use Garland's quadric error matrix to analyze the global distribution of surface details. Based on this analysis, a local retriangulation achieves adaptive sampling by restoring detailed areas with cell split operations while further simplifying smooth areas with edge collapse operations. For a given triangle budget, BR preserves surface details significantly better than uniform sampling algorithms such as uniform clustering. Like uniform clustering, our algorithm has linear running time and small memory requirement.en_US
dc.publisherBlackwell Publishers Ltd and the Eurographics Associationen_US
dc.titleAn Adaptive Sampling Scheme for Out-of-Core Simplificationen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume21en_US
dc.description.number2en_US
dc.identifier.doi10.1111/1467-8659.00571en_US
dc.identifier.pages111-118en_US


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