关键词: 异常检测, 自监督学习, 点云, 计算机视觉, 重构网络

Abstract: As industrial production environments become more intricate, the demand for 3D point cloud industrial anomaly detection is increasing. Although the two-dimensional anomaly detection method based on pre-trained feature extractor is effective, the generalization ability of three-dimensional point cloud pre-training network is limited, which leads to the poor effect of this kind of point cloud anomaly detection method. To bolster the performance of three-dimensional point cloud anomaly detection, Point-ReAD, an innovative method based on point cloud reconstruction, was proposed. Point-ReAD comprises an anomaly simulation module, a point cloud reconstruction network, and an anomaly discrimination module. During training, anomalous point clouds were created from normal point cloud data by the anomaly simulation module, which were then fed into the point cloud reconstruction network, with the normal point clouds serving as self-supervised signals to guide the learning process. The point cloud reconstruction network specifically incorporates the Group Attention Module (GAM), which is meticulously designed to seamlessly integrate intricate structural information from point clouds, thereby enhancing the efficacy of capturing geometric and semantic features in point clouds. In the inference phase, the test point clouds were introduced into the reconstruction network to generate reconstructed point clouds, and anomalies were identified by the anomaly discrimination module by comparing the pre- and post-reconstruction point clouds. The experimental results showed that Point-ReAD achieves point cloud-level AUROC and point-level AUPRO of 95.49% and 94.66%, respectively, on the MVTec 3D-AD dataset, indicating an overall performance improvement compared to methods like M3DM (Multi-3D-Memory) and 3DRÆM (3D Discriminatively trained Reconstruction Anomaly Embedding Model).

Key words: anomaly detection, self- supervised learning, point cloud, computer vision, reconstruction network