  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.
