What is neural implicit surface reconstruction using signed distance functions?

Neural SDF models represent 3D surfaces as continuous signed distance functions learned by neural networks.

Signed Distance Functions (SDFs) define a surface as the zero level set of a function that outputs distance to the nearest surface. Neural networks learn this function from data, enabling smooth and high-resolution 3D reconstruction. Unlike mesh-based methods, SDFs provide continuous and differentiable geometry representations.