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
(\xgy{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), $360^{\circ}$ 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:this https URL

高精度点云异常检测，Real3D-AD 数据集，3D 异常检测数据集，Reg3D-AD，基于配准的 3D 异常检测方法