PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using
  Local-Global Features
Baozhu Zhao Qiwei Xiong Xiaohan Zhang Jingfeng Guo Qi Liu Xiaofen Xing Xiangmin Xu
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.