3D Shape representation has substantial effects on 3D shape reconstruction.
Primitive-based representations approximate a 3D shape mainly by a set of
simple implicit primitives, but the low geometrical complexity of the
primitives limits the shape resolution. Moreover, setting a sufficient number
of primitives for an arbitrary shape is challenging. To overcome these issues,
we propose a constrained implicit algebraic surface as the primitive with few
learnable coefficients and higher geometrical complexities and a deep neural
network to produce these primitives. Our experiments demonstrate the
superiorities of our method in terms of representation power compared to the
state-of-the-art methods in single RGB image 3D shape reconstruction.
Furthermore, we show that our method can semantically learn segments of 3D
shapes in an unsupervised manner. The code is publicly available from
this https URL .

本论文中，我们提出了基于约束隐式代数曲面和深度神经网络的方法，以实现对高复杂度 3D 形状的更准确重建和分割，相较于现有单 RGB 图像 3D 形状重建技术，该方法表现出更强的表现力和性能，可无监督地进行学习。