  Unsupervised methods for reconstructing structures face significant
challenges in capturing the geometric details with consistent structures among
diverse shapes of the same category. To address this issue, we present a novel
unsupervised structural reconstruction method, named DPF-Net, based on a new
Deformable Primitive Field (DPF) representation, which allows for high-quality
shape reconstruction using parameterized geometric primitives. We design a
two-stage shape reconstruction pipeline which consists of a primitive
generation module and a primitive deformation module to approximate the target
shape of each part progressively. The primitive generation module estimates the
explicit orientation, position, and size parameters of parameterized geometric
primitives, while the primitive deformation module predicts a dense deformation
field based on a parameterized primitive field to recover shape details. The
strong shape prior encoded in parameterized geometric primitives enables our
DPF-Net to extract high-level structures and recover fine-grained shape details
consistently. The experimental results on three categories of objects in
diverse shapes demonstrate the effectiveness and generalization ability of our
DPF-Net on structural reconstruction and shape segmentation.
