Multimodal Industrial Anomaly Detection via Uni-Modal and Cross-Modal Fusion

Constructing comprehensive multimodal feature representations from RGB images (RGB) and point clouds (PT) in 2D–3D multimodal anomaly detection (MAD) methods is very important to reveal various types of industrial anomalies. For multimodal representations, most of the existing MAD methods often consider the explicit spatial correspondence between the modality-specific features extracted from RGB and PT through space-aligned fusion, while overlook the implicit interaction relationships between them. In this study, we propose a uni-modal and cross-modal fusion (UCF) method, which comprehensively incorporates the implicit relationships within and between modalities in multimodal representations. Specifically, UCF first establishes uni-modal and cross-modal embeddings to capture intramodal and intermodal relationships through uni-modal reconstruction and cross-modal mapping. Then, an adaptive nonequal fusion method is proposed to develop fusion embeddings, with the aim of preserving the primary features and reducing interference of the uni-modal and cross-modal embeddings. Finally, uni-modal, cross-modal, and fusion embeddings are all collaborated to reveal anomalies existing in different modalities. Experiments conducted on the MVTec 3D-AD benchmark and the real-world surface mount inspection demonstrate that the proposed UCF outperforms existing approaches, particularly in precise anomaly localization.