构建首个高分辨率3D缺陷检测数据集，实现实时精准检测。
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
- 用可扩展管道生成逼真细微3D缺陷数据
- 2577个点云，每帧50万点，缺陷<1%总点数
- 新框架实时推理超20帧/秒，适合工业部署
在工业点云分析中，检测细微异常需要高分辨率空间数据，但现有基准多采用低分辨率输入。为弥合这一差距，我们提出一种可扩展的生成管道，用于创建真实且细微的3D异常数据。基于此，我们构建了首个高分辨率3D异常检测数据集MiniShift，包含2,577个点云，每个点云含500,000个点，异常部分占总点数不足1%。我们进一步提出Simple3D框架，融合多尺度邻域描述符（MSND）与局部特征空间聚合（LFSA），以极小计算开销捕捉复杂几何细节，实现超过20帧/秒的实时推理。在MiniShift及现有基准上的广泛评估表明，Simple3D在准确率与速度上均优于现有方法，凸显高分辨率数据与高效特征聚合对实际3D异常检测的关键作用。
原文摘要 · Abstract (English)
In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1\% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.
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