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

arXiv:2403.01804v1 Announce Type: new
Abstract: 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 …

abstract anomaly anomaly detection applications arxiv autonomous autonomous driving banks cloud cs.cv data detection detection methods driving feature features foundation global industrial memory multiple set three-dimensional training type unsupervised