通过多视角重建提升3D异常检测的全局信息感知能力
Multi-View Reconstruction with Global Context for 3D Anomaly Detection
- 将高分辨率点云转为多视角图像，保留完整细节
- 在Real3D-AD上实现89.6%物体级与95.7%点级AU-ROC
- 适合工业质检中高精度3D异常检测场景
3D异常检测在工业质量检验中至关重要。现有方法虽取得显著进展，但在高精度3D异常检测任务中因全局信息不足导致性能下降。为此，我们提出多视角重建（MVR）方法，可无损地将高分辨率点云转换为多视角图像，并采用基于重建的异常检测框架以增强全局信息学习。大量实验表明，MVR在Real3D-AD基准上取得了89.6%的物体级AU-ROC和95.7%的点级AU-ROC，验证了其有效性。
原文摘要 · Abstract (English)
3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6\% object-wise AU-ROC and 95.7\% point-wise AU-ROC on the Real3D-AD benchmark.
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