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.

我们提出了一种基于新的可变形基元场（DPF）表示的名为 DPF-Net 的新型无监督结构重建方法，该方法能够使用参数化的几何基元进行高质量的形状重建。我们设计了一个包含原始生成模块和原始变形模块的两阶段形状重建流程，逐步逼近每个部分的目标形状。实验结果表明，我们的 DPF-Net 在结构重建和形状分割方面具有有效性和广泛适用性。