Unsupervised industrial anomaly detection aims to train a model capable of identifying diverse anomalous patterns by utilizing only normal samples. Numerous investigations have confirmed the effectiveness of such paradigms for surface anomaly detection using only 2D images. To better capture structural anomalies, recent studies have investigated Multimodal Unsupervised Industrial Anomaly Detection by jointly utilizing 2D images and 3D point cloud. Existing methods either ignore the complementarity between different modalities or consume a lot of storage space to learn and store normal features, making it difficult to balance efficient multi-modal feature utilization with computational efficiency. This paper proposes a novel and efficient unsupervised multimodal anomaly detection framework to fully exploit information from dual-modality data. By modeling latent semantic consistency of normal samples across modalities, the method detects cross-modal consistency deviations during testing for anomaly localization. Simultaneously, lightweight memory banks are separately constructed for each modality, capturing intra-modal feature inconsistencies to provide a complementary anomaly identification perspective parallel to cross-modal detection. Extensive experiments demonstrate that our framework, through simultaneously considering both inter and intra-modal consistency, achieves state-of-the-art (SOTA) detection and segmentation performance on the MVTec 3D-AD dataset with lower computational costs and faster inference speed, while maintaining robust advantages in few-shot setting.

Anomaly detection plays a pivotal role in industrial quality assurance processes, with cross-domain problems, exemplified by the model upgrade from RGB to 3D, being prevalent in real-world scenarios yet remaining systematically underexplored. To address the severe challenges posed by the extreme lack of datasets in target domain, we retain the knowledge from source models and explore a novel solution for anomaly detection through cross-domain learning, introducing HyperTrans. Targeting few-shot scenarios, HyperTrans centers around hypergraphs to model the relationship of the limited patch features and employs a perturbation-rectification-scoring architecture. The domain perturbation module injects and adapts channel-level statistical perturbations, mitigating style shifts during domain transfer. Subsequently, a residual hypergraph restoration module utilizes a cross-domain hypergraph to capture higher-order correlations in patches and align them across domains. Ultimately, with feature patterns exhibiting reduced domain shifts, an inter-domain scoring module aggregates similarity information between patches and normal patterns within the multi-domain subhypergraphs to make an integrated decision, generating multi-level anomaly predictions. Extensive experiments demonstrate that HyperTrans offers significant advantages in anomaly classification and anomaly segmentation tasks, outperforming state-of-the-art non-cross-domain methods in image-wise ROCAUC by 13%, 12%, and 15% in 1-shot, 2-shot, and 5-shot settings on MVTec3D AD.