Anomaly Factory 3D：无监督3D异常检测中多样化伪异常合成的模块化框架
Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection
AI总结：
提出模块化框架AF3AD，通过局部PCA框架中的参数化变形模型合成多样化伪异常，增强无监督3D异常检测训练数据，在多个数据集上提升检测与定位性能。
摘要：
在3D点云中检测和定位缺陷具有挑战性，因为异常样本稀缺且多样，而训练通常仅限于正常数据。我们提出Anomaly Factory 3D (AF3AD)，一个模块化框架，从正常点云合成多样化的伪异常，以扩展依赖于伪异常的无监督3D异常检测方法的训练数据。AF3AD使用在局部PCA框架中定义的中心条件参数化变形模型，具有核控制的空间衰减、各向异性、方向门控以及法向/切向位移场，从而支持广泛的几何缺陷预设。我们通过将AF3AD与偏移预测检测器和基于重建的异常检测方法集成，展示了其易用性和有效性，表明AF3AD可跨检测范式迁移。在AnomalyShapeNet和Real3D-AD上的实验显示，在对象级和点级检测与定位方面均有一致改进，并通过预设组消融实验和噪声鲁棒性分析得到支持。AF3AD设计为独立的合成工具，以促进在不同3D异常检测范式中的采用。代码可在github.com/vpc-ccg/AF3AD获取。
英文摘要：
Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies. AF3AD uses a center-conditioned parametric deformation model defined in local PCA frames, with kernel-controlled spatial falloff, anisotropy, directional gating, and normal/tangential displacement fields, enabling a broad set of geometric defect presets. We demonstrate its ease-of-use and effectiveness by integrating AF3AD with an offset-prediction detector and a reconstruction-based anomaly detection method, showing that AF3AD transfers across detection paradigms. Experiments on AnomalyShapeNet and Real3D-AD show consistent improvements in object- and point-level detection and localization, supported by ablations on preset groups and robustness under noise. AF3AD is designed as a standalone synthesis tool to facilitate adoption across different 3D anomaly detection paradigms. Code is available at github.com/vpc-ccg/AF3AD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
