ASDGen: A Shape Dataset Generator using a Simulated CAD Process

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dc.contributor.authorKomar, Alexanderen_US
dc.contributor.authorRakuschek, Julianen_US
dc.contributor.authorMeszlender, Daviden_US
dc.contributor.authorLackner, Sebastianen_US
dc.contributor.authorBarzegar Khalilsaraei, Saeedehen_US
dc.contributor.authorAugsdörfer, Ursulaen_US
dc.contributor.editorComino Trinidad, Marcen_US
dc.contributor.editorMancinelli, Claudioen_US
dc.contributor.editorMaggioli, Filippoen_US
dc.contributor.editorRomanengo, Chiaraen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorGiorgi, Danielaen_US
dc.date.accessioned2025-11-21T07:28:31Z
dc.date.available2025-11-21T07:28:31Z
dc.date.issued2025
dc.description.abstractNeural networks have shown great promise in 3D applications like shape analysis, object recognition, and design optimization. Machine learning methods depend on high-quality, structured datasets. While databases exist for general 3D shapes, there is a lack of databases tailored for subdivision surface representations. To address this, we introduce ASDGen, an algorithm to generate quadrilateral meshes through a sequence of CAD operations arbitrarily applied to an initial user-defined seed mesh. The resulting meshes are guaranteed to be manifold and can serve as control meshes for generating Catmull-Clark subdivision surfaces. The algorithm may be employed to generate large sets of synthetic shape data represented as quadrilateral meshes of varying degree of refinement, along with all CAD operations applied to a seed mesh to create the shape. The resulting data is ideal to be employed for data-driven analysis of subdivision surfaces. In addition to the shape-data generator, we provide a robust pipeline for extracting various differential shape properties as metadata, e.g. curvature and complexity measures, and for converting these meshes into signed distance fields. We generate a sample dataset of Catmull-Clark subdivision shapes which we make publicly available together with the generator. To demonstrate the potential of ASDGen, present two learning-based applications: a neural network model trained to predict mesh complexity and a prediction of maximum curvature points from the signed distance field of the shape. Our work lays the groundwork for a new class of learning problems rooted in CAD-inspired geometry, and provides both the tools and data necessary to support further research in this domain.en_US
dc.description.sectionheadersDatasets
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20251334
dc.identifier.isbn978-3-03868-296-7
dc.identifier.issn2617-4855
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20251334
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/stag20251334
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleASDGen: A Shape Dataset Generator using a Simulated CAD Processen_US
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