超越几何：面向3D逻辑异常检测的基准测试与一致性推理

中文摘要 AI 辅助

现有的3D工业异常检测主要针对局部几何偏差。相比之下，许多工业异常违反了物体级别的设计或装配规则，我们将其定义为3D逻辑异常。为应对这些挑战，我们引入了工业逻辑异常检测数据集（ILGAD），这是首个专用于工业点云中逻辑异常的可扩展基准。ILGAD包含来自15个类别的2,774个样本，具有点级标注，并涵盖存在性、规格、姿态和装配状态错误。为检测此类3D逻辑异常，我们提出了一种一致性推理框架，该框架评估局部几何、结构覆盖和空间关系是否符合正常设计。该框架可检测几何变化、不支持的预期结构以及异常局部排列。在ILGAD、Anomaly-ShapeNet和IEC3D上的实验表明，该框架在物体级检测和点级定位方面表现优越，显示出其能有效检测逻辑异常并泛化至传统几何缺陷。

英文摘要

Existing 3D industrial anomaly detection mainly targets local geometric deviations. In contrast, many industrial anomalies violate object-level design or assembly rules, which we define as 3D logical anomalies. To address these challenges, we introduce the Industrial Logical Anomaly Detection Dataset (ILGAD), the first scalable benchmark dedicated to logical anomalies in industrial point clouds. ILGAD contains 2,774 samples from 15 categories with point-level annotations and covers existence, specification, pose, and assembly-state errors. To detect such 3D logical anomalies, we propose a consistency reasoning framework that assesses whether local geometry, structure coverage, and spatial relations conform to the normal design. The framework detects geometric changes, unsupported expected structures, and abnormal local arrangements. Experiments on ILGAD, Anomaly-ShapeNet, and IEC3D demonstrate superior object-level detection and point-level localization, showing that the framework effectively detects logical anomalies and generalizes to conventional geometric defects.

发表机构

• School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）
• National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）

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