利用视点间几何约束提升多视角工业缺陷检测效果
Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion
- 引入对极几何约束注意力模块，引导跨视角特征融合
- 在MVTec AD数据集上达到新最优，显著优于现有方法
- 适合需要高精度多相机缺陷检测的工业场景
多相机系统为工业缺陷检测提供了更丰富的上下文信息。然而，传统方法独立处理每个视角，忽视了视点间的互补信息。现有方法虽采用数据驱动的跨视角注意力进行特征融合，但未利用多相机设置特有的几何特性。本文提出一种受对极几何约束的注意力模块，指导跨视角融合，实现更有效的信息聚合。为进一步提升跨视角注意力性能，提出一种受记忆库异常检测启发的预训练策略，促使正常特征形成多个局部聚类，并通过多视角感知的负样本合成进行正则化。实验表明，该对极引导的多视角异常检测框架在当前最先进多视角异常检测数据集上超越现有方法。
原文摘要 · Abstract (English)
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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