SuperSDF: Sparse SDF Super-Resolution for Surface Extraction

Signed Distance Fields (SDFs) are a powerful volumetric representation for 3D geometry. Recent advances in surface generation from SDFs increasingly rely on learnable surface representations and direct mesh supervision. In this work, we challenge this trend and show that high-quality surface reconstruction can be achieved by learning to refine the volumetric signal itself. We present SuperSDF, a learning-based approach for sparse SDF super-resolution that operates directly in SDF space, without auxiliary surface representations or mesh-level supervision. Using a sparse voxel neural network restricted to a narrow band near the surface, our method predicts high-resolution signed-distance values from coarse inputs in a scalable and resolution-agnostic manner. Standard isosurface extraction algorithms can then process the resulting super-resolved SDFs to produce accurate, detailed surface meshes. Our results show that learning-based SDF upsampling alone is sufficient to recover fine geometric details missed by classical interpolation and prior reconstruction methods. Compared to state-of-the-art machine learning approaches, our method generates higher-fidelity surfaces at a fraction of the computational cost and scales to volumetric resolutions that were previously impractical.