We introduce Explicit Neural Surfaces (ENS), an efficient surface
reconstruction method that learns an explicitly defined continuous surface from
multiple views. We use a series of neural deformation fields to progressively
transform a continuous input surface to a target shape. By sampling meshes as
discrete surface proxies, we train the deformation fields through efficient
differentiable rasterization, and attain a mesh-independent and smooth surface
representation. By using Laplace-Beltrami eigenfunctions as an intrinsic
positional encoding alongside standard extrinsic Fourier features, our approach
can capture fine surface details. ENS trains 1 to 2 orders of magnitude faster
and can extract meshes of higher quality compared to implicit representations,
whilst maintaining competitive surface reconstruction performance and real-time
capabilities. Finally, we apply our approach to learn a collection of objects
in a single model, and achieve disentangled interpolations between different
shapes, their surface details, and textures.

本篇研究提出了一种名为 ENS 的表面重构方法，使用一系列神经形变场逐步将连续输入表面转换到目标形状，通过 Laplace-Beltrami 本征函数和标准外部傅里叶特征进行表征，具有实时性能和高质量的网格抽取能力。