通过关键点引导聚类，实现高分辨率3D点云的精准异常定位。
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
- 用多原型对齐与聚类差异分析，实现结构化对比。
- 在Real3D-AD上达到当前最优的物体级和点级检测效果。
- 关键点选作为聚类中心，提升局部特征捕捉能力。
高分辨率3D点云在工业检测中能有效发现细微结构异常，但其密集且不规则的特性带来计算成本高、空间错位敏感及局部结构差异难捕捉等挑战。本文提出一种基于配准的异常检测框架，结合多原型对齐与聚类级差异分析，实现精确的3D异常定位。具体而言，将测试样本注册到多个正常原型以进行直接结构比较；在点云上进行聚类，计算测试样本与各聚类内原型特征间的相似性；不随机选取聚类中心，而是采用关键点引导策略，选择几何信息丰富的点作为聚类中心，确保聚类聚焦于特征丰富区域，提升基于距离的比较意义与稳定性。在Real3D-AD基准上的大量实验表明，该方法在物体级和点级异常检测上均达到当前最优性能，即使仅使用原始特征亦可实现优异效果。
原文摘要 · Abstract (English)
High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significant challenges, including high computational cost, sensitivity to spatial misalignment, and difficulty in capturing localized structural differences. This paper introduces a registration-based anomaly detection framework that combines multi-prototype alignment with cluster-wise discrepancy analysis to enable precise 3D anomaly localization. Specifically, each test sample is first registered to multiple normal prototypes to enable direct structural comparison. To evaluate anomalies at a local level, clustering is performed over the point cloud, and similarity is computed between features from the test sample and the prototypes within each cluster. Rather than selecting cluster centroids randomly, a keypoint-guided strategy is employed, where geometrically informative points are chosen as centroids. This ensures that clusters are centered on feature-rich regions, enabling more meaningful and stable distance-based comparisons. Extensive experiments on the Real3D-AD benchmark demonstrate that the proposed method achieves state-of-the-art performance in both object-level and point-level anomaly detection, even using only raw features.
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