  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.
