We present a novel neural network architecture, termed Decomposer-Composer,
for semantic structure-aware 3D shape modeling. Our method utilizes an
auto-encoder-based pipeline, and produces a novel factorized shape embedding
space, where the semantic structure of the shape collection translates into a
data-dependent sub-space factorization, and where shape composition and
decomposition become simple linear operations on the embedding coordinates. We
further propose to model shape assembly using an explicit learned part
deformation module, which utilizes a 3D spatial transformer network to perform
an in-network volumetric grid deformation, and which allows us to train the
whole system end-to-end. The resulting network allows us to perform part-level
shape manipulation, unattainable by existing approaches. Our extensive ablation
study, comparison to baseline methods and qualitative analysis demonstrate the
improved performance of the proposed method.

本文提出了一种名为 Decomposer-Composer 的新型神经网络结构，用于基于语义结构的 3D 形状建模，采用自编码器技术，在嵌入空间上进行分解 - 合成操作，并通过引入显式的学习部分变形模块，使得该网络能够实现部件级的形状操作，具有较高的性能表现。