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This paper introduces SGANet, a novel framework for unsupervised multi-view anomaly detection that tackles feature inconsistency issues arising from viewpoint and modality variations. SGANet employs a Selective Cross-view Feature Refinement Module (SCFRM) for cross-view feature interaction, a Semantic-Structural Patch Alignment (SSPA) module for semantic alignment across modalities, and a Multi-View Geometric Alignment (MVGA) module for geometric correspondence across viewpoints. Experiments on SiM3D and Eyecandies datasets demonstrate state-of-the-art anomaly detection and localization performance.
Achieve state-of-the-art anomaly detection by explicitly aligning semantic and geometric features across multiple views and modalities.
Multi-view anomaly detection aims to identify surface defects on complex objects using observations captured from multiple viewpoints. However, existing unsupervised methods often suffer from feature inconsistency arising from viewpoint variations and modality discrepancies. To address these challenges, we propose a Semantic and Geometric Alignment Network (SGANet), a unified framework for multimodal multi-view anomaly detection that effectively combines semantic and geometric alignment to learn physically coherent feature representations across viewpoints and modalities. SGANet consists of three key components. The Selective Cross-view Feature Refinement Module (SCFRM) selectively aggregates informative patch features from adjacent views to enhance cross-view feature interaction. The Semantic-Structural Patch Alignment (SSPA) enforces semantic alignment across modalities while maintaining structural consistency under viewpoint transformations. The Multi-View Geometric Alignment (MVGA) further aligns geometrically corresponding patches across viewpoints. By jointly modeling feature interaction, semantic and structural consistency, and global geometric correspondence, SGANet effectively enhances anomaly detection performance in multimodal multi-view settings. Extensive experiments on the SiM3D and Eyecandies datasets demonstrate that SGANet achieves state-of-the-art performance in both anomaly detection and localization, validating its effectiveness in realistic industrial scenarios.
