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dc.contributor.authorGiannelli, Carlottaen_US
dc.contributor.authorImperatore, Sofiaen_US
dc.contributor.authorMantzaflaris, Angelosen_US
dc.contributor.authorMokriš, Dominiken_US
dc.contributor.editorBanterle, Francescoen_US
dc.contributor.editorCaggianese, Giuseppeen_US
dc.contributor.editorCapece, Nicolaen_US
dc.contributor.editorErra, Ugoen_US
dc.contributor.editorLupinetti, Katiaen_US
dc.contributor.editorManfredi, Gildaen_US
dc.date.accessioned2023-11-12T15:37:40Z
dc.date.available2023-11-12T15:37:40Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-235-6
dc.identifier.issn2617-4855
dc.identifier.urihttps://doi.org/10.2312/stag.20231301
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20231301
dc.description.abstractReconstruction of highly accurate CAD models from point clouds is both paramount and challenging in industries such as aviation. Due to the acquisition process, this kind of data can be scattered and affected by noise, yet the reconstructed geometric models are required to be compact and smooth, while simultaneously capturing key geometric features of the engine parts. In this paper, we present an iterative moving parameterization approach, which consists of alternating steps of surface fitting, parameter correction, and adaptive refinement using truncated hierarchical B-splines (THB-splines). We revisit two existing surface fitting methods, a global least squares approximation and a hierarchical quasi-interpolation scheme, both based on THB-splines. At each step of the adaptive loop, we update the parameter locations by solving a non-linear optimization problem to infer footpoints of the point cloud on the current fitted surface. We compare the behavior of different optimization settings for the critical task of distance minimization, by also relating the effectiveness of the correction step to the quality of the initial parameterization. In addition, we apply the proposed approach in the reconstruction of aircraft engine components from scanned point data. It turns out that the use of moving parameterization instead of fixed parameter values, when suitably combined with the adaptive spline loop, can significantly improve the resulting surfaces, thus outperforming state-of-the-art hierarchical spline model reconstruction schemes.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Parametric curve and surface models
dc.subjectComputing methodologies
dc.subjectParametric curve and surface models
dc.titleLeveraging Moving Parameterization and Adaptive THB-Splines for CAD Surface Reconstruction of Aircraft Engine Componentsen_US
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.description.sectionheadersRepresentation of 3D shapes
dc.identifier.doi10.2312/stag.20231301
dc.identifier.pages125-134
dc.identifier.pages10 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License