Unsupervised Feature Metric-Based Multimodal Anomaly Detection Method

Aiming at the problems of complex and diverse types of abnormal data and the difficulty of collection and annotation in actual industrial scenarios, a multimodal anomaly detection method based on unsupervised feature measurement is proposed. Firstly, a rotation-invariant 3D point cloud feature extraction network is constructed to solve the problem of data pose differences and enhance the model's ability to capture 3D features; Secondly, an unsupervised multimodal feature modeling module is designed to achieve the extraction of richer feature information; Finally, the feature measurement module is introduced to improve the accuracy of anomaly detection and location. The comparison results with the existing advanced methods on the MVTec-3D dataset show that this method performs excellently in anomaly detection, location and resistance to interference from abnormal data. The ablation experiments further verified the effectiveness of the proposed 3D feature extractor and multimodal fusion method.