Single-image 3D shape reconstruction is an important and long-standing
problem in computer vision. A plethora of existing works is constantly pushing
the state-of-the-art performance in the deep learning era. However, there
remains a much more difficult and under-explored issue on how to generalize the
learned skills over unseen object categories that have very different shape
geometry distributions. In this paper, we bring in the concept of compositional
generalizability and propose a novel framework that could better generalize to
these unseen categories. We factorize the 3D shape reconstruction problem into
proper sub-problems, each of which is tackled by a carefully designed neural
sub-module with generalizability concerns. The intuition behind our formulation
is that object parts (slates and cylindrical parts), their relationships
(adjacency and translation symmetry), and shape substructures (T-junctions and
a symmetric group of parts) are mostly shared across object categories, even
though object geometries may look very different (e.g. chairs and cabinets).
Experiments on PartNet show that we achieve superior performance than
state-of-the-art. This validates our problem factorization and network designs.

本文在单目图像 3D 形状重建领域引入了组合通用性的概念，并提出了一个新的框架来 better generalize 到那些有着非常不同的形状几何分布的看不见的目标类别。实验证明，我们的方法在 PartNet 中达到了比最先进的方法更高的性能，验证了我们的问题分解和网络设计的有效性。