NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

Understand

We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs.

• Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision, easily get trapped in local minima, and therefore struggle with the reconstruction of objects with severe self-occlusion or thin structures.
• Meanwhile, recent neural methods for novel view synthesis, such as NeRF and its variants, use volume rendering to produce a neural scene representation with robustness of optimization, even for highly complex objects.
• However, extracting high-quality surfaces from this learned implicit representation is difficult because there are not sufficient surface constraints in the representation.