Computer graphics, 3D computer vision and robotics communities have produced
multiple approaches to representing 3D geometry for rendering and
reconstruction. These provide trade-offs across fidelity, efficiency and
compression capabilities. In this work, we introduce DeepSDF, a learned
continuous Signed Distance Function (SDF) representation of a class of shapes
that enables high quality shape representation, interpolation and completion
from partial and noisy 3D input data. DeepSDF, like its classical counterpart,
represents a shape's surface by a continuous volumetric field: the magnitude of
a point in the field represents the distance to the surface boundary and the
sign indicates whether the region is inside (-) or outside (+) of the shape,
hence our representation implicitly encodes a shape's boundary as the
zero-level-set of the learned function while explicitly representing the
classification of space as being part of the shapes interior or not. While
classical SDF's both in analytical or discretized voxel form typically
represent the surface of a single shape, DeepSDF can represent an entire class
of shapes. Furthermore, we show state-of-the-art performance for learned 3D
shape representation and completion while reducing the model size by an order
of magnitude compared with previous work.

介绍了 DeepSDF 方法，它是用于高品质形状表示、插值和完成的一种学习连续有符号距离函数（SDF）的表示，将 3D 几何表示为体积场，并可表示整个形状类。DeepSDF 方法相比以往方法降低了一个数量级的模型大小，同时获得了学习 3D 形状表示和完成方面的最先进性能。