NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
This work addresses the problem of intuitive shape control for 3D modeling in computer graphics, offering an incremental improvement over existing neural implicit methods.
The paper tackles the challenge of modeling neural implicit surfaces with sparse geometric control, such as unstructured 3D curve sketches, by proposing NeuVAS, a variational approach that incorporates a smoothness term and sharp feature modeling, resulting in significant advantages over state-of-the-art methods as demonstrated in comprehensive comparisons.
Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeling the shape of a neural implicit surface, especially as the zero-level set of a neural signed distance function (SDF), with sparse geometric control is still a challenging task. Sparse input shape control typically includes 3D curve networks or, more generally, 3D curve sketches, which are unstructured and cannot be connected to form a curve network, and therefore more difficult to deal with. While 3D curve networks or curve sketches provide intuitive shape control, their sparsity and varied topology pose challenges in generating high-quality surfaces to meet such curve constraints. In this paper, we propose NeuVAS, a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control, including unstructured 3D curve sketches as well as connected 3D curve networks. Specifically, we introduce a smoothness term based on a functional of surface curvatures to minimize shape variation of the zero-level set surface of a neural SDF. We also develop a new technique to faithfully model G0 sharp feature curves as specified in the input curve sketches. Comprehensive comparisons with the state-of-the-art methods demonstrate the significant advantages of our method.