发表机构
School of Computer Science, Wuhan University; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ); School of Information and Software Engineering, East China Jiaotong University; School of Computer Science and Technology, Dalian University of Technology（武汉大学计算机科学学院; 广东省人工智能与数字经济实验室（深圳）; 华东交通大学信息工程学院; 大连理工大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D点云异常检测中真实异常样本稀缺等问题，提出PA3AD框架，采用物理启发式伪异常生成策略及原型特征引导，通过关键创新有效学习分布变化，实验证明其检测性能优于现有方法。
AI 中文摘要
3D点云异常检测在工业制造中至关重要，但因真实异常样本稀缺且获取成本高而面临挑战。无异常的训练数据阻碍检测方法学习正常与异常实例间的判别特征。为此提出PA3AD框架，引入物理启发式伪异常生成策略从正常数据创建合理的异常样本，通过权重共享机制纳入原型特征以引导模型捕捉分布变化。具体通过物理启发模块和动量更新原型等创新解决真实异常稀缺问题，实验表明该方法优于现有方法。
英文摘要
3D point cloud anomaly detection plays a vital role in industrial manufacturing, yet it faces significant challenges due to the scarcity and high acquisition cost of real anomalous samples. The inherently anomaly-free training data further hinders detection methods from effectively learning discriminative features between normal and abnormal instances. To address these issues, we propose PA3AD, a novel framework that introduces a physics-inspired pseudo-anomaly generation strategy to create physically plausible anomalous samples from normal data. Additionally, we incorporate prototype features via a weight-sharing mechanism to guide the model in capturing the distribution shifts between normal and anomalous samples. Specifically, PA3AD introduces two key innovations to tackle the scarcity of real anomalies. First, a physics-inspired module generates diverse pseudo-anomalous point clouds from normal data via multi-physics modeling. Second, momentum-updated prototypes and a difference-aware fusion block capture stable normal representations and their discrepancies with pseudo-anomalies. This design effectively learns distribution shifts, achieving superior detection performance. Extensive experiments on the Anomaly-ShapeNet and Real3D-AD datasets demonstrate that our method consistently outperforms existing state-of-the-art approaches. Our code will be made publicly available at https://github.com/NingxiaoJian/PA3AD.
Comments20 pages; already accepted by Pattern Recognition
