Three-dimensional point cloud anomaly detection that aims to detect anomaly
data points from a training set serves as the foundation for a variety of
applications, including industrial inspection and autonomous driving. However,
existing point cloud anomaly detection methods often incorporate multiple
feature memory banks to fully preserve local and global representations, which
comes at the high cost of computational complexity and mismatches between
features. To address that, we propose an unsupervised point cloud anomaly
detection framework based on joint local-global features, termed PointCore. To
be specific, PointCore only requires a single memory bank to store local
(coordinate) and global (PointMAE) representations and different priorities are
assigned to these local-global features, thereby reducing the computational
cost and mismatching disturbance in inference. Furthermore, to robust against
the outliers, a normalization ranking method is introduced to not only adjust
values of different scales to a notionally common scale, but also transform
densely-distributed data into a uniform distribution. Extensive experiments on
Real3D-AD dataset demonstrate that PointCore achieves competitive inference
time and the best performance in both detection and localization as compared to
the state-of-the-art Reg3D-AD approach and several competitors.

提出了一个基于联合局部 - 全局特征、命名为 PointCore 的无监督点云异常检测框架，它仅需一个存储器来保存局部和全局表示，并为这些局部 - 全局特征分配不同的优先级，从而降低了计算复杂性和推理中的匹配干扰。通过引入归一化排名方法来抵抗异常值，调整不同尺度的值以达到概念上的公共尺度，并将密集分布的数据转化为均匀分布。在 Real3D-AD 数据集上的广泛实验表明，与现有方法 Reg3D-AD 和其他竞争方法相比，PointCore 在检测和定位方面不仅具有竞争力的推理时间，还具有最佳性能。

PointCore: 高效的无监督点云异常检测器，利用局部 - 全局特征

PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using  Local-Global Features

The Swin Transformer image super-resolution reconstruction network only
relies on the long-range relationship of window attention and shifted window
attention to explore features. This mechanism has two limitations. On the one
hand, it only focuses on global features while ignoring local features. On the
other hand, it is only concerned with spatial feature interactions while
ignoring channel features and channel interactions, thus limiting its
non-linear mapping ability. To address the above limitations, this paper
proposes enhanced Swin Transformer modules via alternating aggregation of
local-global features. In the local feature aggregation stage, this paper
introduces shift convolution to realize the interaction between local spatial
information and channel information. This paper proposes a block sparse global
perception module in the global feature aggregation stage. This module
organizes the spatial information first, then sends the recombination
information into a spatial gating unit to implement the further interaction of
spatial and channel information. Then, a multi-scale self-attention module and
a low-parameter residual channel attention module are introduced to realize
information aggregation at different scales. Finally, the proposed network is
validated on five publicly available datasets. The experimental results show
that the proposed network outperforms the other state-of-the-art
super-resolution networks.

本论文通过交替聚合局部全局特征，引入了位移卷积实现局部空间信息和通道信息的交互，并提出了块稀疏全局感知模块以进一步实现空间和通道信息的交互。同时，引入了多尺度自注意模块和低参数残差通道注意模块，实现了不同尺度上的信息聚合。在五个公开数据集上验证了提出的网络，在超分辨率方面超过了其他最先进的网络。