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arXiv · 2503.11088

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

Yifan Liu·Xun Xu·Shijie Li·Jingyi Liao·Xulei Yang
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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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Yifan Liu, Xun Xu, Shijie Li, Jingyi Liao, Xulei Yang. 2025-03-14. Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion. https://arxiv.org/abs/2503.11088

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