用双模态生成缺陷数据，提升3D工业异常检测精度
DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection
- 设计双模态增强法，合成逼真3D缺陷样本
- 在MVTec和Eyescandies数据集上检测精度领先
- 适合需要高精度3D异常检测的工业场景
在2D工业异常检测中，合成异常样本已被证明是有效的自监督策略。但在多模态异常检测，尤其是涉及3D与RGB图像的场景中，该方法仍鲜有探索。本文提出一种新颖的双模态3D异常合成方法，简单且能有效模拟3D缺陷特征。结合该合成方法，我们设计了一种基于重构的判别式异常检测网络，其中双模态判别器融合两模态的原始与重构嵌入以实现异常检测。此外，我们引入增强丢弃机制，提升判别器的泛化能力。大量实验表明，本方法在检测精度上优于现有最先进方法，在MVTec 3D-AD和Eyescandies数据集上均达到具有竞争力的分割性能。
原文摘要 · Abstract (English)
Synthesizing anomaly samples has proven to be an effective strategy for self-supervised 2D industrial anomaly detection. However, this approach has been rarely explored in multi-modality anomaly detection, particularly involving 3D and RGB images. In this paper, we propose a novel dual-modality augmentation method for 3D anomaly synthesis, which is simple and capable of mimicking the characteristics of 3D defects. Incorporating with our anomaly synthesis method, we introduce a reconstruction-based discriminative anomaly detection network, in which a dual-modal discriminator is employed to fuse the original and reconstructed embedding of two modalities for anomaly detection. Additionally, we design an augmentation dropout mechanism to enhance the generalizability of the discriminator. Extensive experiments show that our method outperforms the state-of-the-art methods on detection precision and achieves competitive segmentation performance on both MVTec 3D-AD and Eyescandies datasets.
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