发表机构
Jeonbuk National University; Korea Institute of Industrial Technology (KITECH)（全北国立大学; 韩国产业技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D异常检测中正常区域过度异常响应和物体边界附近误报问题，提出M2P-AD模型，通过M2P模块学习原型、BE模块提取边界，结合BSR策略校准分数，在多个数据集上实现最优性能，提升异常检测准确性与稳定性。
AI 中文摘要
3D异常检测最近成为计算机视觉中的一个重要研究课题。尽管现有方法取得了高性能，但正常区域的过度异常响应和物体边界附近的误报仍是未解决的挑战。为应对这些挑战，我们提出了一种新颖的3D异常检测模型M2P-AD，它能有效建模正常特征分布，抑制正常区域的过度异常分数和物体边界附近的误报。具体而言，我们引入了一个内存到原型（M2P）模块，从正常特征嵌入中学习代表性原型以保留物体的重要结构信息。此外，集成了一个边界提取（BE）模块来识别物体边界，并应用边界感知分数细化（BSR）策略通过合并边界特征来重新校准异常分数。该方法在Real3D-AD、Anomaly-ShapeNet和MulSen-AD上进行了评估，取得了当前最优性能。定性结果表明，正常区域的过度异常分数减少，物体边界附近的误报得到抑制，从而实现更准确、稳定的异常定位。结果表明，该方法能实现更可靠的3D异常检测，并为实际工业环境提供了一个强大的解决方案。
英文摘要
3D anomaly detection has recently emerged as an important research topic in computer vision. Although existing methods have achieved high performance, excessive anomaly responses in normal regions and false positives near object boundaries remain unresolved challenges. To address these challenges, we propose a novel 3D anomaly detection model, Memory-to-Prototype Anomaly Detection (M2P-AD), which effectively models the distribution of normal features while suppressing excessive anomaly scores in normal regions and false positives near object boundaries. Specifically, we introduce a Memory-to-Prototype (M2P) module that learns representative prototypes from normal feature embeddings to preserve important structural information of objects. In addition, a Boundary extraction (BE) module is integrated to identify object boundaries, and a Boundary-aware score refinement (BSR) strategy is applied to recalibrate anomaly scores by incorporating boundary characteristics. The proposed method is evaluated on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, achieving state-of-the-art performance. Qualitative results demonstrate that excessive anomaly scores in normal regions are reduced and false positives near object boundaries are suppressed, resulting in more accurate and stable anomaly localization. The results indicate that the proposed approach enables more reliable 3D anomaly detection and provides a robust solution applicable to real-world industrial environments.
Comments16 pages, 6 figures
