Komar, AlexanderRakuschek, JulianMeszlender, DavidLackner, SebastianBarzegar Khalilsaraei, SaeedehAugsdörfer, UrsulaComino Trinidad, MarcMancinelli, ClaudioMaggioli, FilippoRomanengo, ChiaraCabiddu, DanielaGiorgi, Daniela2025-11-212025-11-212025978-3-03868-296-72617-4855https://doi.org/10.2312/stag.20251334https://diglib.eg.org/handle/10.2312/stag20251334Neural 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.Attribution 4.0 International License10.2312/stag.2025133410 pages