Djuren, TobiasAlexa, MarcMasia, BelenThies, Justus2026-04-172026-04-1720261467-8659https://diglib.eg.org/handle/10.1111/cgf70326https://doi.org/10.1111/cgf70326We present a framework for learning compactly supported basis functions that define tangent continuous surfaces based on coarse irregular triangle meshes. The basis functions are represented as MLPs. Smoothness of the basis functions is achieved by using the values of Loop basis functions as the parameterization of the surface. Post-multiplying the value of the MLP with the Loop basis yields smooth compact support. We show that this approach works similar or better than Neural Subdivision in terms of recreating given geometry, while the runtime scales better with surface resolution and can be evaluated at arbitrary resolution.CC-BY-4.0Computing methodologies → Parametric curve and surface models; Machine learning approaches;Computing methodologies → Parametric curve and surface modelsMachine learning approaches10.1111/cgf.7032613 pages