  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. In NeuS, we propose to represent a surface
as the zero-level set of a signed distance function (SDF) and develop a new
volume rendering method to train a neural SDF representation. We observe that
the conventional volume rendering method causes inherent geometric errors (i.e.
bias) for surface reconstruction, and therefore propose a new formulation that
is free of bias in the first order of approximation, thus leading to more
accurate surface reconstruction even without the mask supervision. Experiments
on the DTU dataset and the BlendedMVS dataset show that NeuS outperforms the
state-of-the-arts in high-quality surface reconstruction, especially for
objects and scenes with complex structures and self-occlusion.
