We introduce SDM-NET, a deep generative neural network which produces
structured deformable meshes. Specifically, the network is trained to generate
a spatial arrangement of closed, deformable mesh parts, which respect the
global part structure of a shape collection, e.g., chairs, airplanes, etc. Our
key observation is that while the overall structure of a 3D shape can be
complex, the shape can usually be decomposed into a set of parts, each
homeomorphic to a box, and the finer-scale geometry of the part can be
recovered by deforming the box. The architecture of SDM-NET is that of a
two-level variational autoencoder (VAE). At the part level, a PartVAE learns a
deformable model of part geometries. At the structural level, we train a
Structured Parts VAE (SP-VAE), which jointly learns the part structure of a
shape collection and the part geometries, ensuring a coherence between global
shape structure and surface details. Through extensive experiments and
comparisons with the state-of-the-art deep generative models of shapes, we
demonstrate the superiority of SDM-NET in generating meshes with visual
quality, flexible topology, and meaningful structures, which benefit shape
interpolation and other subsequently modeling tasks.

SDM-NET 是一种深度生成神经网络，用于生成符合形状集合全局部分结构的有结构变形的网格，并通过部分 VAE 在部分几何形状和结构 VAE 的联合学习来确保全局形状结构和表面详情之间的一致性。

SDM-NET: 结构可变网格的深度生成网络

SDM-NET: Deep Generative Network for Structured Deformable Mesh

We present a learning framework for abstracting complex shapes by learning to
assemble objects using 3D volumetric primitives. In addition to generating
simple and geometrically interpretable explanations of 3D objects, our
framework also allows us to automatically discover and exploit consistent
structure in the data. We demonstrate that using our method allows predicting
shape representations which can be leveraged for obtaining a consistent parsing
across the instances of a shape collection and constructing an interpretable
shape similarity measure. We also examine applications for image-based
prediction as well as shape manipulation.

提出一种利用三维成体基元组装物品的学习框架，可以生成简单且几何可解释的三维物品解释，利用该方法可以获得形状表征形式、构建可解释的形状相似性测量，以及进行基于图像的预测和形状操作。