Qiang Bai

Learning Compact Latent Space for Representing Neural Signed Distance Functions with High-fidelity Geometry Details

Neural signed distance functions (SDFs) have been a vital representation to represent 3D shapes or scenes with neural networks. An SDF is an implicit function that can query signed distances at specific coordinates for recovering a 3D surface. Although implicit functions work well on a single shape