  We present a method for generating high-quality watertight manifold meshes
from multi-view input images. Existing volumetric rendering methods are robust
in optimization but tend to generate noisy meshes with poor topology.
Differentiable rasterization-based methods can generate high-quality meshes but
are sensitive to initialization. Our method combines the benefits of both
worlds; we take the geometry initialization obtained from neural volumetric
fields, and further optimize the geometry as well as a compact neural texture
representation with differentiable rasterizers. Through extensive experiments,
we demonstrate that our method can generate accurate mesh reconstructions with
faithful appearance that are comparable to previous volume rendering methods
while being an order of magnitude faster in rendering. We also show that our
generated mesh and neural texture reconstruction is compatible with existing
graphics pipelines and enables downstream 3D applications such as simulation.
Project page: https://sarahweiii.github.io/neumanifold/
