NeuPPS: Neural Piecewise Parametric Surfaces

Lei Yang, Yongqing Liang, Xin Li, Congyi Zhang, Guying Lin, Cheng Lin, Alla Sheffer, Scott Schaefer, John Keyser, Wenping Wang

Abstract: Piecewise parametric surfaces have long been established as prevalent geometric representations; however, they often require surface refinement or sophisticated quadrangulation to accurately represent complex geometries. Geometric deep learning has shown that neural networks can provide greater representational power than conventional methods. Nevertheless, approaches using a single parametric surface for shape fitting struggle to capture fine-grained geometric details, while multi-patch methods fail to ensure seamless connections between adjacent patches. We present Neural Piecewise Parametric Surfaces (NeuPPS), the first piecewise neural surface representation that allows for coarse patch layouts composed of arbitrary n-sided surface patches to model complex surface geometries with high precision, offering enhanced flexibility compared with traditional parametric surfaces. This new surface …