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。

基于非参数贝叶斯推断的基元形状抽象

Primitive-based Shape Abstraction via Nonparametric Bayesian Inference

Analyzing the underlying structure of multiple time-sequences provides
insights into the understanding of social networks and human activities. In
this work, we present the \emph{Bayesian nonparametric Poisson process
allocation} (BaNPPA), a latent-function model for time-sequences, which
automatically infers the number of latent functions. We model the intensity of
each sequence as an infinite mixture of latent functions, each of which is
obtained using a function drawn from a Gaussian process. We show that a
technical challenge for the inference of such mixture models is the
unidentifiability of the weights of the latent functions. We propose to cope
with the issue by regulating the volume of each latent function within a
variational inference algorithm. Our algorithm is computationally efficient and
scales well to large data sets. We demonstrate the usefulness of our proposed
model through experiments on both synthetic and real-world data sets.

本文提出了一种针对时间序列的具有潜在函数模型的贝叶斯非参数泊松过程分配（BaNPPA）方法，通过模拟一个高斯过程中的函数，利用无限混合的潜在函数表示强度来自动推断潜在函数数量，随后通过变分推断算法调节潜在函数的体积来应对权重不可区分的问题，该算法高效，并且在合成和真实数据集的实验中取得了良好的效果。