We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task may fail to capture the geometric structures of anomalies due to uncertainty in lighting conditions or shooting angles. PD-REAL consists entirely of Play-Doh models for 15 object categories and focuses on the analysis of potential benefits from 3D information in a controlled environment. Specifically, objects are first created with six types of anomalies, such as dent, crack, or perforation, and then photographed under different lighting conditions to mimic real-world inspection scenarios. To demonstrate the usefulness of 3D information, we use a commercially available RealSense camera to capture RGB and depth images. Compared to the existing 3D dataset for AD tasks, the data acquisition of PD-REAL is significantly cheaper, easily scalable and easier to control variables. Extensive evaluations with state-of-the-art AD algorithms on our dataset demonstrate the benefits as well as challenges of using 3D information. Our dataset can be downloaded from https://github.com/Andy-cs008/PD-REAL

翻译：我们提出PD-REAL，一个用于三维领域无监督异常检测（AD）的新型大规模数据集。该数据集的提出基于以下事实：在AD任务中，仅依赖二维表示可能因光照条件或拍摄角度的不确定性而无法捕获异常的几何结构。PD-REAL完全由15个物体类别的Play-Doh模型组成，专注于在受控环境中分析三维信息的潜在优势。具体而言，物体首先被制造出六种类型的异常（如凹痕、裂纹或穿孔），然后在不同光照条件下进行拍摄，以模拟真实工业检测场景。为证明三维信息的实用性，我们使用商用RealSense相机采集RGB图像和深度图像。与现有用于AD任务的三维数据集相比，PD-REAL的数据采集成本显著更低、易于扩展且变量控制更简便。通过采用最先进的AD算法对我们的数据集进行广泛评估，验证了利用三维信息的优势与挑战。我们的数据集可从https://github.com/Andy-cs008/PD-REAL 下载。