We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It is inspired by real-world visual inspection scenarios in which a model has to detect various types of defects on manufactured products, even if it is trained only on anomaly-free data. There are defects that manifest themselves as anomalies in the geometric structure of an object. These cause significant deviations in a 3D representation of the data. We employed a high-resolution industrial 3D sensor to acquire depth scans of 10 different object categories. For all object categories, we present a training and validation set, each of which solely consists of scans of anomaly-free samples. The corresponding test sets contain samples showing various defects such as scratches, dents, holes, contaminations, or deformations. Precise ground-truth annotations are provided for every anomalous test sample. An initial benchmark of 3D anomaly detection methods on our dataset indicates a considerable room for improvement.

翻译：我们为未受监督的异常探测和定位任务引入了第一个全面的三维数据集。它受现实世界的视觉检查情景的启发,模型必须检测制成品的各类缺陷,即使它只接受无异常数据的培训。有些缺陷表现为物体几何结构中的异常。这些缺陷在数据的立体表示中造成了显著偏差。我们使用了高分辨率的工业三维传感器来获取10个不同对象类别的深度扫描。对于所有对象类别,我们展示了一套培训和验证工具,其中每一种都只包括无异常样品的扫描。相应的测试设备含有显示各种缺陷的样本,如刮痕、凹痕、洞穴、污染或变形。为每个异常试验样品提供了精确的地面图解。我们数据集上3D异常探测方法的初步基准表明有相当大的改进空间。