Partitioning a polygonal mesh into meaningful parts can be challenging. Many
applications require decomposing such structures for further processing in
computer graphics. In the last decade, several methods were proposed to tackle
this problem, at the cost of intensive computational times. Recently, machine
learning has proven to be effective for the segmentation task on 3D structures.
Nevertheless, these state-of-the-art methods are often hardly generalizable and
require dividing the learned model into several specific classes of objects to
avoid overfitting. We present a data-driven approach leveraging deep learning
to encode a mapping function prior to mesh segmentation for multiple
applications. Our network reproduces a neighborhood map using our knowledge of
the \textsl{Shape Diameter Function} (SDF) method using similarities among
vertex neighborhoods. Our approach is resolution-agnostic as we downsample the
input meshes and query the full-resolution structure solely for neighborhood
contributions. Using our predicted SDF values, we can inject the resulting
structure into a graph-cut algorithm to generate an efficient and robust mesh
segmentation while considerably reducing the required computation times.

该论文提出了一种数据驱动的方法，利用深度学习编码映射函数进行网格分割，该方法利用顶点邻域之间的相似性重建邻域映射，并将预测的 SDF 值注入到图割算法中，从而大幅度降低计算时间。

用于高效网格分割的神经形状直径函数

Neural Shape Diameter Function for Efficient Mesh Segmentation

3D deep learning is a growing field of interest due to the vast amount of
information stored in 3D formats. Triangular meshes are an efficient
representation for irregular, non-uniform 3D objects. However, meshes are often
challenging to annotate due to their high geometrical complexity. Specifically,
creating segmentation masks for meshes is tedious and time-consuming.
Therefore, it is desirable to train segmentation networks with limited-labeled
data. Self-supervised learning (SSL), a form of unsupervised representation
learning, is a growing alternative to fully-supervised learning which can
decrease the burden of supervision for training. We propose SSL-MeshCNN, a
self-supervised contrastive learning method for pre-training CNNs for mesh
segmentation. We take inspiration from traditional contrastive learning
frameworks to design a novel contrastive learning algorithm specifically for
meshes. Our preliminary experiments show promising results in reducing the
heavy labeled data requirement needed for mesh segmentation by at least 33%.

提出了一种自监督对比学习方法 SSL-MeshCNN 用于预训练 CNNs 从而解决三角形网格难以准确定义的问题，该方法表现出降低网格分割所需标记数据的可行性

自监督对比表示学习用于三维网格分割

Self-Supervised Contrastive Representation Learning for 3D Mesh  Segmentation

We address the problem of unsupervised learning of complex articulated object
models from 3D range data. We describe an algorithm whose input is a set of
meshes corresponding to different configurations of an articulated object. The
algorithm automatically recovers a decomposition of the object into
approximately rigid parts, the location of the parts in the different object
instances, and the articulated object skeleton linking the parts. Our algorithm
first registers allthe meshes using an unsupervised non-rigid technique
described in a companion paper. It then segments the meshes using a graphical
model that captures the spatial contiguity of parts. The segmentation is done
using the EM algorithm, iterating between finding a decomposition of the object
into rigid parts, and finding the location of the parts in the object
instances. Although the graphical model is densely connected, the object
decomposition step can be performed optimally and efficiently, allowing us to
identify a large number of object parts while avoiding local maxima. We
demonstrate the algorithm on real world datasets, recovering a 15-part
articulated model of a human puppet from just 7 different puppet
configurations, as well as a 4 part model of a fiexing arm where significant
non-rigid deformation was present.

本文介绍了一种基于无监督学习的算法，可以从 3D 点云数据中自动分解复杂的关节式物体模型，定位其各部分的位置，并还原其关节连接，方法通过无刚性变形技术配合 EM 算法和图像模型实现，可在避免局部最大值的情况下获取大量物体部分。