High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset, addressing the limitations in the field. With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection to date. Real3D-AD surpasses existing 3D anomaly detection datasets available regarding point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect prototype. Additionally, we present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection. To address this, we propose Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations. Extensive experiments on the Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility and accessibility, we provide the Real3D-AD dataset, benchmark source code, and Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD.

翻译：高精度点云异常检测是识别先进加工与精密制造缺陷的金标准。尽管该领域已取得若干方法论进展，但数据集的稀缺和系统性基准的缺失阻碍了其发展。我们提出Real3D-AD——一个具有挑战性的高精度点云异常检测数据集，旨在解决该领域的局限性。该数据集包含1254个高分辨率三维物体，每个物体的点数从四万到数百万不等，是迄今为止规模最大的高精度三维工业异常检测数据集。Real3D-AD在点云分辨率（0.0010mm-0.0015mm）、360度覆盖范围和完美原型方面均超越现有三维异常检测数据集。此外，我们为Real3D-AD提供了全面的基准测试，揭示了高精度点云异常检测中基线方法的缺失。为解决这一问题，我们提出Reg3D-AD，一种基于配准的三维异常检测方法，该方法融合了能够保存局部和全局表征的新型特征记忆库。在Real3D-AD数据集上的大量实验凸显了Reg3D-AD的有效性。为促进可重复性和可访问性，我们在网站https://github.com/M-3LAB/Real3D-AD上提供了Real3D-AD数据集、基准源码及Reg3D-AD。