面向可泛化三维异常检测的关系不一致性建模
Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling
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中文总结 AI 辅助
提出关系不一致性建模框架，通过边感知图细化和簇偏差建模学习类别无关缺陷线索，在Anomaly-ShapeNet和Real3D-AD上实现域内与跨域持续最优。
中文摘要 AI 辅助
三维异常检测（3DAD）旨在识别点云数据中的缺陷区域，是工业检测系统中的关键组成部分。现有方法以正常性为中心——学习正常样本的分布并将偏差视为异常——而没有显式建模缺陷的构成要素。这导致决策边界模糊，误报和漏报增加，尤其在统一和跨域设置中，多样化的正常分布进一步模糊了边界。我们提出了一种关系不一致性建模框架，将缺陷表征为相邻结构间几何一致性的违反。我们的方法通过设计为受控关系违反的伪异常来学习类别无关的缺陷线索，由两个关键模块实例化：用于编码局部区域间几何关系的边感知图细化（EGR），以及用于识别在其结构同行组内关系不兼容区域的簇偏差建模（CDM）。在Anomaly-ShapeNet和Real3D-AD上的大量实验表明，在域内和跨域设置中均持续优于先前的最先进方法，验证了学习显式的、基于关系的缺陷标准用于三维异常检测的有效性。项目页面：此https URL。
英文摘要
3D anomaly detection (3DAD) aims to identify defective regions in point cloud data, serving as a critical component in industrial inspection systems. Existing methods are normality-centered -- learning the distribution of normal samples and treating deviations as anomalies -- without explicitly modeling what constitutes a defect. This leads to ambiguous decision boundaries with increased false positives and negatives, particularly in unified and cross-domain settings where diverse normal distributions further blur the boundaries. We propose a relational inconsistency modeling framework that characterizes defects as violations of geometric consistency among neighboring structures. Our approach learns category-agnostic defect cues through pseudo-anomalies designed as controlled relational violations, instantiated by two key modules: Edge-aware Graph Refinement (EGR) for encoding geometric relationships among local regions, and Cluster-Deviation Modeling (CDM) for identifying regions that are relationally incompatible within their structural peer group. Extensive experiments on Anomaly-ShapeNet and Real3D-AD demonstrate consistent improvements over prior state-of-the-art methods in both in-domain and cross-domain settings, validating the effectiveness of learning an explicit, relation-based defect criterion for 3D anomaly detection. Project page: https://visualsciencelab-khu.github.io/GRIM_project/.
发表机构
- Kyung Hee University（庆熙大学）
机构由 AI 辅助整理，请以论文原文为准。
