The task of shape abstraction with semantic part consistency is challenging due to the complex geometries of natural objects. Recent methods learn to represent an object shape using a set of simple primitives to fit the target. \textcolor{black}{However, in these methods, the primitives used do not always correspond to real parts or lack geometric flexibility for semantic interpretation.} In this paper, we investigate salient and efficient primitive descriptors for accurate shape abstractions, and propose \textit{Deep Deformable Models (DDMs)}. DDM employs global deformations and diffeomorphic local deformations. These properties enable DDM to abstract complex object shapes with significantly fewer primitives that offer broader geometry coverage and finer details. DDM is also capable of learning part-level semantic correspondences due to the differentiable and invertible properties of our primitive deformation. Moreover, DDM learning formulation is based on dynamic and kinematic modeling, which enables joint regularization of each sub-transformation during primitive fitting. Extensive experiments on \textit{ShapeNet} demonstrate that DDM outperforms the state-of-the-art in terms of reconstruction and part consistency by a notable margin.

本论文研究了精确形状抽象的显著和高效的基本描述符，提出了一种名为Deep Deformable Models (DDMs)的方法。DDM利用全局变形和微分同胚本地变形来抽象复杂的物体形状，使用较少的基本图元提供更广泛的几何覆盖和更精细的细节，并能学习基于部位的语义对应关系。DDM在形状重建和部位一致性方面的实验结果表明，较现有方法具有显著优势。

深度可变形模型：通过部分一致性学习3D形状抽象