VLDFNet: Views-Graph and Latent Feature Disentangled Fusion Network for Multimodal Industrial Anomaly Detection

Accurate detection and precise localization of anomalies during precision component manufacturing are essential to maintaining high product quality. Multimodal Industrial Anomaly Detection (MIAD) harnesses data from diverse sensors to effectively identify and pinpoint defects in industrial products. Recent MIAD approaches have made significant progress but often ignore point cloud data global contextual semantics and modality-specific information, resulting in incomplete representation of point cloud and inadequate multimodal fusion. To confront these issues head-on, we propose a robust feature representation and comprehensive multimodal feature fusion network (VLDFNet) for anomaly detection in industrial high-precision components. VLDFNet mainly consists of a point cloud views-graph representation model and a disentangled feature latent space fusion module. Specifically, the point cloud views-graph representation model explores spatial locations and semantic relationships between views using multi-level graph fusion. The multimodal disentangled feature latent space fusion module disentanglies multimodal features into shared and specific representations to mitigate the omission of modality-specific information. VLDFNet introduces a cross-modal shared feature interaction strategy to extract coherent semantic information by aligning and integrating cross-modal features. Comprehensive experimental results on multiple datasets demonstrate that our method significantly outperforms existing approaches in detection accuracy.