We introduce Explicit Neural Surfaces (ENS), an efficient smooth surface representation that directly encodes topology with a deformation field from a known base domain. We apply this representation to reconstruct explicit surfaces from multiple views, where we use a series of neural deformation fields to progressively transform the base domain into a target shape. By using meshes as discrete surface proxies, we train the deformation fields through efficient differentiable rasterization. Using a fixed base domain allows us to have Laplace-Beltrami eigenfunctions as an intrinsic positional encoding alongside standard extrinsic Fourier features, with which our approach can capture fine surface details. Compared to implicit surfaces, ENS trains faster and has several orders of magnitude faster inference times. The explicit nature of our approach also allows higher-quality mesh extraction whilst maintaining competitive surface reconstruction performance and real-time capabilities.

翻译：我们提出显式神经曲面（ENS），这是一种高效的平滑曲面表示方法，通过从已知基础域出发的变形场直接编码拓扑结构。我们将该表示应用于多视图显式曲面重建，利用一系列神经变形场逐步将基础域变换为目标形状。通过将网格用作离散曲面代理，我们借助高效可微光栅化技术训练变形场。固定基础域使我们能够将拉普拉斯-贝尔特拉米特征函数作为内在位置编码，同时结合标准外在傅里叶特征，从而捕获精细曲面细节。与隐式曲面相比，ENS训练速度更快且推理速度快数个数量级。我们的显式方法还能在保持竞争性曲面重建性能与实时能力的同时，提取更高质量的网格。