  Recent progress in neural implicit functions has set new state-of-the-art in
reconstructing high-fidelity 3D shapes from a collection of images. However,
these approaches are limited to closed surfaces as they require the surface to
be represented by a signed distance field. In this paper, we propose NeAT, a
new neural rendering framework that can learn implicit surfaces with arbitrary
topologies from multi-view images. In particular, NeAT represents the 3D
surface as a level set of a signed distance function (SDF) with a validity
branch for estimating the surface existence probability at the query positions.
We also develop a novel neural volume rendering method, which uses SDF and
validity to calculate the volume opacity and avoids rendering points with low
validity. NeAT supports easy field-to-mesh conversion using the classic
Marching Cubes algorithm. Extensive experiments on DTU, MGN, and Deep Fashion
3D datasets indicate that our approach is able to faithfully reconstruct both
watertight and non-watertight surfaces. In particular, NeAT significantly
outperforms the state-of-the-art methods in the task of open surface
reconstruction both quantitatively and qualitatively.
