{"ID":6536108,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T02:56:36.47817413Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10544","arxiv_id":"2607.10544","title":"Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection","abstract":"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.","short_abstract":"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 betw...","url_abs":"https://arxiv.org/abs/2607.10544","url_pdf":"https://arxiv.org/pdf/2607.10544v1","authors":"[\"Jian Ning\",\"Qin Zou\",\"Linchun Wu\",\"Yuanhao Yue\",\"Kunmo Li\",\"Shoubin Chen\",\"Zhongyuan Wang\"]","published":"2026-07-12T03:11:49Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":614133,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T01:21:01.169441415Z","DeletedAt":null,"paper_id":6536108,"paper_url":"https://arxiv.org/abs/2607.10544","paper_title":"Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection","repo_url":"https://github.com/NingxiaoJian/PA3AD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}