仅用少量正常样本实现跨类别3D异常检测，无需训练
DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples
- 将点云转为多视角真实深度图，用冻结的CLIP编码器提取特征
- 在少样本下优于现有方法，零微调仍保持高精度
- 适合工业质检等实际场景中快速部署的异常检测需求
针对3D点云的跨类别异常检测任务，旨在仅用少量正常样本判断未知物体是否属于目标类别。现有方法多依赖类别特定训练，限制了其在少样本场景下的灵活性。本文提出DMP-3DAD，一种基于多视图真实深度图投影的无训练框架。通过将点云转换为固定数量的真实深度图像，利用冻结的CLIP视觉编码器提取多视角表征，并基于加权特征相似性进行异常检测，无需任何微调或类别相关适配。在ShapeNetPart数据集上的大量实验表明，DMP-3DAD在少样本设置下达到领先性能，验证了该方法在实际跨类别3D异常检测中的有效性。
原文摘要 · Abstract (English)
Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category using only a few normal examples. Most existing methods rely on category-specific training, which limits their flexibility in few-shot scenarios. In this paper, we propose DMP-3DAD, a training-free framework for cross-category 3D anomaly detection based on multi-view realistic depth map projection. Specifically, by converting point clouds into a fixed set of realistic depth images, our method leverages a frozen CLIP visual encoder to extract multi-view representations and performs anomaly detection via weighted feature similarity, which does not require any fine-tuning or category-dependent adaptation. Extensive experiments on the ShapeNetPart dataset demonstrate that DMP-3DAD achieves state-of-the-art performance under few-shot setting. The results show that the proposed approach provides a simple yet effective solution for practical cross-category 3D anomaly detection.
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