Multimodal Industrial Anomaly Detection via Hybrid Fusion
2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code is available at https://github.com/nomewang/M3DM.
Code (1)
Tasks
3D Anomaly DetectionAnomaly DetectionContrastive LearningRGB+3D Anomaly Detection and SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multimodal Industrial Anomaly Detection via Geometric Prior
The purpose of multimodal industrial anomaly detection is to detect complex geometric shape defects such as subtle surface deformations and irregular contours that are difficult to detect in 2D-based methods. However, cu…
Anomaly DetectionMultimodal Real-Time Anomaly Detection and Industrial Applications
This paper presents the design, implementation, and evolution of a comprehensive multimodal room-monitoring system that integrates synchronized video and audio processing for real-time activity recognition and anomaly de…
Activity RecognitionAnomaly DetectionObject DetectionA Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection
In the advancement of industrial informatization, Unsupervised Industrial Anomaly Detection (UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliabili…
Anomaly DetectionRADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection
Multimodal Industrial Anomaly Detection (MIAD), utilizing 3D point clouds and 2D RGB images to identify the abnormal region of products, plays a crucial role in industrial quality inspection. However, the conventional MI…
Anomaly DetectionPhilosophyHDM: Hybrid Diffusion Model for Unified Image Anomaly Detection
Image anomaly detection plays a vital role in applications such as industrial quality inspection and medical imaging, where it directly contributes to improving product quality and system reliability. However, existing m…
Anomaly Detection