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.

翻译：三维点云的跨类别异常检测旨在仅利用少量正常样本，判断未见物体是否属于目标类别。现有方法大多依赖特定类别的训练，这在少样本场景下限制了其灵活性。本文提出DMP-3DAD，一种基于多视角真实深度图投影的无训练跨类别三维异常检测框架。具体而言，通过将点云转换为固定组真实深度图像，本方法利用冻结的CLIP视觉编码器提取多视角表征，并通过加权特征相似度进行异常检测，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在少样本设定下达到了最先进的性能。结果表明，所提方法为实际跨类别三维异常检测提供了一种简单而有效的解决方案。