  Recently, 3D anomaly detection, a crucial problem involving fine-grained
geometry discrimination, is getting more attention. However, the lack of
abundant real 3D anomaly data limits the scalability of current models. To
enable scalable anomaly data collection, we propose a 3D anomaly synthesis
pipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.
Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, basedon
ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40
categories, which provides a rich and varied collection of data, enabling
efficient training and enhancing adaptability to industrial scenarios.
Meanwhile,to enable scalable representation learning for 3D anomaly
localization, we propose a self-supervised method, i.e., Iterative Mask
Reconstruction Network (IMRNet). During training, we propose a geometry-aware
sample module to preserve potentially anomalous local regions during point
cloud down-sampling. Then, we randomly mask out point patches and sent the
visible patches to a transformer for reconstruction-based self-supervision.
During testing, the point cloud repeatedly goes through the Mask Reconstruction
Network, with each iteration's output becoming the next input. By merging and
contrasting the final reconstructed point cloud with the initial input, our
method successfully locates anomalies. Experiments show that IMRNet outperforms
previous state-of-the-art methods, achieving 66.1% in I-AUC on Anomaly-ShapeNet
dataset and 72.5% in I-AUC on Real3D-AD dataset. Our dataset will be released
at https://github.com/Chopper-233/Anomaly-ShapeNet
