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。