Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion

• URL: http://arxiv.org/abs/2503.11088v1
• Date: Fri, 14 Mar 2025 05:02:54 GMT
• Title: Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion
• Authors: Yifan Liu, Xun Xu, Shijie Li, Jingyi Liao, Xulei Yang,
• Abstract summary: We introduce an epipolar geometry-constrained attention module to guide cross-view fusion.<n>To further enhance the potential of cross-view attention, we propose a pretraining strategy inspired by memory bank-based anomaly detection.<n>We demonstrate that our framework outperforms existing methods on the state-of-the-art multi-view anomaly detection dataset.
• Score: 15.819291772583393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-camera systems provide richer contextual information for industrial anomaly detection. However, traditional methods process each view independently, disregarding the complementary information across viewpoints. Existing multi-view anomaly detection approaches typically employ data-driven cross-view attention for feature fusion but fail to leverage the unique geometric properties of multi-camera setups. In this work, we introduce an epipolar geometry-constrained attention module to guide cross-view fusion, ensuring more effective information aggregation. To further enhance the potential of cross-view attention, we propose a pretraining strategy inspired by memory bank-based anomaly detection. This approach encourages normal feature representations to form multiple local clusters and incorporate multi-view aware negative sample synthesis to regularize pretraining. We demonstrate that our epipolar guided multi-view anomaly detection framework outperforms existing methods on the state-of-the-art multi-view anomaly detection dataset.

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