Unlike on images, semantic learning on 3D point clouds using a deep network
is challenging due to the naturally unordered data structure. Among existing
works, PointNet has achieved promising results by directly learning on point
sets. However, it does not take full advantage of a point's local neighborhood
that contains fine-grained structural information which turns out to be helpful
towards better semantic learning. In this regard, we present two new operations
to improve PointNet with a more efficient exploitation of local structures. The
first one focuses on local 3D geometric structures. In analogy to a convolution
kernel for images, we define a point-set kernel as a set of learnable 3D points
that jointly respond to a set of neighboring data points according to their
geometric affinities measured by kernel correlation, adapted from a similar
technique for point cloud registration. The second one exploits local
high-dimensional feature structures by recursive feature aggregation on a
nearest-neighbor-graph computed from 3D positions. Experiments show that our
network can efficiently capture local information and robustly achieve better
performances on major datasets. Our code is available at
this http URL

本文提出了两个新的操作，帮助 PointNet 更高效地利用局部结构以提高 3D 点云的语义学习效果，并通过实验证明，我们的模型可以捕获局部信息并在主要数据集上表现更好。

利用核相关和图池化挖掘点云局部结构

Mining Point Cloud Local Structures by Kernel Correlation and Graph  Pooling

We propose a non-parametric link prediction algorithm for a sequence of graph
snapshots over time. The model predicts links based on the features of its
endpoints, as well as those of the local neighborhood around the endpoints.
This allows for different types of neighborhoods in a graph, each with its own
dynamics (e.g, growing or shrinking communities). We prove the consistency of
our estimator, and give a fast implementation based on locality-sensitive
hashing. Experiments with simulated as well as five real-world dynamic graphs
show that we outperform the state of the art, especially when sharp
fluctuations or non-linearities are present.

提出了一个基于非参数方法的链接预测算法，该算法结合了端点特征和端点周围邻居的特征，可以分析不同类型的图并给出了一种基于局部敏感哈希的快速实现，实验证明该方法表现优于现有算法。