  3D anomaly detection plays a crucial role in monitoring parts for localized
inherent defects in precision manufacturing. Embedding-based and
reconstruction-based approaches are among the most popular and successful
methods. However, there are two major challenges to the practical application
of the current approaches: 1) the embedded models suffer the prohibitive
computational and storage due to the memory bank structure; 2) the
reconstructive models based on the MAE mechanism fail to detect anomalies in
the unmasked regions. In this paper, we propose R3D-AD, reconstructing
anomalous point clouds by diffusion model for precise 3D anomaly detection. Our
approach capitalizes on the data distribution conversion of the diffusion
process to entirely obscure the input's anomalous geometry. It step-wisely
learns a strict point-level displacement behavior, which methodically corrects
the aberrant points. To increase the generalization of the model, we further
present a novel 3D anomaly simulation strategy named Patch-Gen to generate
realistic and diverse defect shapes, which narrows the domain gap between
training and testing. Our R3D-AD ensures a uniform spatial transformation,
which allows straightforwardly generating anomaly results by distance
comparison. Extensive experiments show that our R3D-AD outperforms previous
state-of-the-art methods, achieving 73.4% Image-level AUROC on the Real3D-AD
dataset and 74.9% Image-level AUROC on the Anomaly-ShapeNet dataset with an
exceptional efficiency.
