Accurate 3D shape abstraction from a single 2D image is a long-standing
problem in computer vision and graphics. By leveraging a set of primitives to
represent the target shape, recent methods have achieved promising results.
However, these methods either use a relatively large number of primitives or
lack geometric flexibility due to the limited expressibility of the primitives.
In this paper, we propose a novel bi-channel Transformer architecture,
integrated with parameterized deformable models, termed DeFormer, to
simultaneously estimate the global and local deformations of primitives. In
this way, DeFormer can abstract complex object shapes while using a small
number of primitives which offer a broader geometry coverage and finer details.
Then, we introduce a force-driven dynamic fitting and a cycle-consistent
re-projection loss to optimize the primitive parameters. Extensive experiments
on ShapeNet across various settings show that DeFormer achieves better
reconstruction accuracy over the state-of-the-art, and visualizes with
consistent semantic correspondences for improved interpretability.

本文提出了一种名为 DeFormer 的新型双通道 Transformer 架构，通过参数化的可变形模型同时估计原语的全局和局部变形，以实现复杂对象形状的抽象化，在使用较少的原语的同时提供更广泛的几何覆盖范围和更精细的细节，并引入了力驱动的动态拟合和循环一致的投影损失来优化原语参数，在 ShapeNet 上的广泛实验表明，DeFormer 在重建准确性方面优于现有技术，并且通过一致的语义对应关系实现了可解释性的提升。

DeFormer: 集成可变形模型的变压器用于从单张图片中进行三维形状抽象

DeFormer: Integrating Transformers with Deformable Models for 3D Shape  Abstraction from a Single Image

3D shape abstraction has drawn great interest over the years. Apart from
low-level representations such as meshes and voxels, researchers also seek to
semantically abstract complex objects with basic geometric primitives. Recent
deep learning methods rely heavily on datasets, with limited generality to
unseen categories. Furthermore, abstracting an object accurately yet with a
small number of primitives still remains a challenge. In this paper, we propose
a novel non-parametric Bayesian statistical method to infer an abstraction,
consisting of an unknown number of geometric primitives, from a point cloud. We
model the generation of points as observations sampled from an infinite mixture
of Gaussian Superquadric Taper Models (GSTM). Our approach formulates the
abstraction as a clustering problem, in which: 1) each point is assigned to a
cluster via the Chinese Restaurant Process (CRP); 2) a primitive representation
is optimized for each cluster, and 3) a merging post-process is incorporated to
provide a concise representation. We conduct extensive experiments on two
datasets. The results indicate that our method outperforms the state-of-the-art
in terms of accuracy and is generalizable to various types of objects.

本文提出了一种非参数贝叶斯统计方法用于从点云中推断抽象的三维形状，包括未知数量的基本几何基元，并展示了该方法的实验结果比现有方法更准确且具有普适性。