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.

翻译：无监督结构重建方法在捕捉同一类别不同形状间具有一致结构的几何细节方面面临显著挑战。为解决该问题，我们提出一种新型无监督结构重建方法DPF-Net，该方法基于全新的可变形基元场（DPF）表示，能够利用参数化几何基元实现高质量形状重建。我们设计了一个两阶段形状重建流水线，包含基元生成模块与基元变形模块，可逐步逼近目标形状的每个部件。基元生成模块估计参数化几何基元的显式朝向、位置及尺寸参数，而基元变形模块基于参数化基元场预测密集变形场以恢复形状细节。参数化几何基元中蕴含的强形状先验使得DPF-Net能够提取高层结构并一致性地恢复细粒度形状细节。在三个不同形状类别的物体上的实验结果表明，DPF-Net在结构重建与形状分割方面具有有效性和泛化能力。