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dc.contributor.authorChu, Yiyaoen_US
dc.contributor.authorWang, Wenchengen_US
dc.contributor.editorUmetani, Nobuyukien_US
dc.contributor.editorWojtan, Chrisen_US
dc.contributor.editorVouga, Etienneen_US
dc.date.accessioned2022-10-04T06:39:25Z
dc.date.available2022-10-04T06:39:25Z
dc.date.issued2022
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14652
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14652
dc.description.abstractExisting methods for skeleton extraction have limitations in terms of the amount of memory space available, as the model must be allocated to the random access memory. This challenges the treatment of out-of-core models. Although applying out-of-core simplification methods to the model can fit in memory, this would induce distortion of the model surface, and so causing the skeleton to be off-centered or changing the topological structure. In this paper, we propose an efficient out-of-core method for extracting skeletons from large volumetric models. The method takes a volumetric model as input and first computes an out-of-core distance transform. With the distance transform, we generate a medial mesh to capture the prominent features for skeleton extraction, which significantly reduces the data size and facilitates the process of large models. At last, we contract the medial mesh in an out-of-core fashion to generate the skeleton. Experimental results show that our method can efficiently extract high-quality curve skeletons from large volumetric models with small memory usage.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Shape modeling; Shape analysis
dc.subjectComputing methodologies → Shape modeling
dc.subjectShape analysis
dc.titleOut-of-core Extraction of Curve Skeletons for Large Volumetric Modelsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersCurves and Meshes
dc.description.volume41
dc.description.number7
dc.identifier.doi10.1111/cgf.14652
dc.identifier.pages1-12
dc.identifier.pages12 pages


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  • 41-Issue 7
    Pacific Graphics 2022 - Symposium Proceedings

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