Recent surface anomaly detection methods rely on pretrained backbone networks for efficient anomaly detection. On standard RGB anomaly detection benchmarks these methods achieve excellent results but fail on 3D anomaly detection due to a lack of pretrained backbones that suit this domain. Additionally, there is a lack of industrial depth data that would enable the backbone network training that could be used in 3D anomaly detection models. Discriminative anomaly detection methods do not require pretrained networks and are trained using simulated anomalies. The process of simulating anomalies that fit the domain of industrial depth data is not trivial and is necessary for training discriminative methods. We propose a novel 3D anomaly simulation process that follows the natural characteristics of industrial depth data and generates diverse deformations, making it suitable for training discriminative anomaly detection methods. We demonstrate its effectiveness by adapting the DRÆM method to work on 3D anomaly detection, thus obtaining 3DRÆM, a strong discriminative 3D anomaly detection model. The proposed approach achieves excellent results on the MVTec3D anomaly detection benchmark where it achieves state-of-the-art results on both 3D and RGB+3D problem setups, significantly outperforming competing methods.

中文翻译：

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Keep DRÆMing：通过异常模拟进行区分性 3D 异常检测

最近的表面异常检测方法依赖于预训练的骨干网络来进行有效的异常检测。在标准 RGB 异常检测基准上，这些方法取得了出色的结果，但由于缺乏适合该领域的预训练主干，因此在 3D 异常检测上失败。此外，缺乏工业深度数据来支持可用于 3D 异常检测模型的骨干网络训练。判别性异常检测方法不需要预先训练的网络，而是使用模拟异常进行训练。模拟适合工业深度数据领域的异常的过程并不简单，对于训练判别方法是必要的。我们提出了一种新颖的 3D 异常模拟过程，该过程遵循工业深度数据的自然特征并生成不同的变形，使其适合训练判别性异常检测方法。我们通过将 DRÆM 方法应用于 3D 异常检测来证明其有效性，从而获得 3DRÆM，一种强辨别力的 3D 异常检测模型。所提出的方法在 MVTec3D 异常检测基准上取得了优异的结果，在 3D 和 RGB+3D 问题设置上均取得了最先进的结果，显着优于竞争方法。