We propose a novel neural architecture for representing 3D surfaces, which
harnesses two complementary shape representations: (i) an explicit
representation via an atlas, i.e., embeddings of 2D domains into 3D; (ii) an
implicit-function representation, i.e., a scalar function over the 3D volume,
with its levels denoting surfaces. We make these two representations
synergistic by introducing novel consistency losses that ensure that the
surface created from the atlas aligns with the level-set of the implicit
function. Our hybrid architecture outputs results which are superior to the
output of the two equivalent single-representation networks, yielding smoother
explicit surfaces with more accurate normals, and a more accurate implicit
occupancy function. Additionally, our surface reconstruction step can directly
leverage the explicit atlas-based representation. This process is
computationally efficient, and can be directly used by differentiable
rasterizers, enabling training our hybrid representation with image-based
losses.

本论文提出了一种新的神经体系结构来表示 3D 表面，通过引入一些创新的一致性损失来保证两个形状表示方式的协同处理，这种混合架构能够比单一表示网络产生更优的结果。