Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
<|reference_start|>Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework: 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.<|reference_end|>
@article{cheng2024towards,
    title={Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection
  Framework},
    author={Yuqi Cheng, Yunkang Cao, Guoyang Xie, Zhichao Lu, Weiming Shen},
    journal={arXiv preprint arXiv:2409.13162},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13162},
    primaryClass={cs.CV}
}