Toward Zero-Shot Point Cloud Anomaly Detection: A Multiview Projection Framework
提出多视图投影框架，将点云转为深度图像，利用预训练视觉语言模型实现零样本异常检测，并通过可学习提示技术提升性能，在工业零件检测中验证了泛化能力。
Detecting anomalies within point clouds is crucial for various industrial applications, but traditional unsupervised methods face challenges due to data acquisition costs, early stage production constraints, and limited generalization across product categories. To overcome these challenges, we introduce the multiview projection (MVP) framework, leveraging pretrained vision-language models (VLMs) to detect anomalies. Specifically, MVP projects point cloud data into multiview depth images, thereby translating point cloud anomaly detection into image anomaly detection. Following zero-shot image anomaly detection methods, pretrained VLMs are utilized to detect anomalies on these depth images. Given that pretrained VLMs are not inherently tailored for zero-shot point cloud anomaly detection and may lack specificity, we propose the integration of learnable visual and adaptive text prompting techniques to fine-tune these VLMs, thereby enhancing their detection performance. Extensive experiments on the MVTec 3-D-AD and Real3D-AD demonstrate our proposed MVP framework’s superior zero-shot anomaly detection performance and the prompting techniques’ effectiveness. Real-world evaluations on automotive plastic part inspection further showcase that the proposed method can also be generalized to practical, unseen scenarios.