{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-zero-shot-point-cloud-anomaly","title":"Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework","arxiv_id":"2409.13162","date":"2024-09-20","proceeding":null,"authors":["Yuqi Cheng","Yunkang Cao","Guoyang Xie","Zhichao Lu","Weiming Shen"],"abstract":"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 Multi-View Projection (MVP) framework, leveraging pre-trained Vision-Language Models (VLMs) to detect anomalies. Specifically, MVP projects point cloud data into multi-view depth images, thereby translating point cloud anomaly detection into image anomaly detection. Following zero-shot image anomaly detection methods, pre-trained VLMs are utilized to detect anomalies on these depth images. Given that pre-trained 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 3D-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. The code is available at https://github.com/hustCYQ/MVP-PCLIP.","url_abs":"https://arxiv.org/abs/2409.13162v1","url_pdf":"https://arxiv.org/pdf/2409.13162v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-zero-shot-point-cloud-anomaly","repo_url":"https://github.com/hustcyq/mvp-pclip","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"towards-zero-shot-point-cloud-anomaly","repo_url":"https://github.com/hustCYQ/GLFM-Multi-class-3DAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"zero-shot-anomaly-detection","task_name":"zero-shot anomaly detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2409.13162","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}