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

Deep Deformable Models: Learning 3D Shape Abstractions with Part  Consistency

Representing complex 3D objects as simple geometric primitives, known as
shape abstraction, is important for geometric modeling, structural analysis,
and shape synthesis. In this paper, we propose an unsupervised shape
abstraction method to map a point cloud into a compact cuboid representation.
We jointly predict cuboid allocation as part segmentation and cuboid shapes and
enforce the consistency between the segmentation and shape abstraction for
self-learning. For the cuboid abstraction task, we transform the input point
cloud into a set of parametric cuboids using a variational auto-encoder
network. The segmentation network allocates each point into a cuboid
considering the point-cuboid affinity. Without manual annotations of parts in
point clouds, we design four novel losses to jointly supervise the two branches
in terms of geometric similarity and cuboid compactness. We evaluate our method
on multiple shape collections and demonstrate its superiority over existing
shape abstraction methods. Moreover, based on our network architecture and
learned representations, our approach supports various applications including
structured shape generation, shape interpolation, and structural shape
clustering.

本文提出了一种无监督形状抽象方法，将点云转换为紧凑的长方体表示，并以分割和长方体形状的联合预测为抽象任务，以自学习的方式加强分割和形状抽象之间的一致性，该方法不需要手动注释点云中的零件。本文通过几种形状集的评估，证明了该方法优于现有的形状抽象方法，支持结构形状生成、形状插值和结构形状聚类等各种应用。