用视觉语言模型+图传播，无训练实现3D点云异常检测
Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation
- 构建类别原型与测试数据的图结构，利用数据流形提升检测能力
- 提出GSP方法，在合成与真实数据集上均超越现有最优结果
- 无需训练、支持少样本，适合实际部署的鲁棒感知场景
3D点云数据中的分布外（OOD）检测仍是挑战，尤其在安全与鲁棒感知至关重要的应用中。尽管2D图像的OOD检测已取得进展，但将其拓展至3D环境面临独特难题。本文提出一种无训练框架，利用视觉语言模型（VLM）实现有效的3D点云OOD检测。通过基于类别原型与测试数据构建图结构，挖掘数据流形特性以增强VLM性能。提出新型图得分传播（GSP）方法，结合提示聚类与自训练负向提示机制，提升基于VLM的OOD评分效果。该方法还可适应少样本场景，具备实际应用灵活性。实验表明，GSP在合成与真实世界数据集上的3D点云OOD检测任务中持续优于当前最优方法。
原文摘要 · Abstract (English)
Out-of-distribution (OOD) detection in 3D point cloud data remains a challenge, particularly in applications where safe and robust perception is critical. While existing OOD detection methods have shown progress for 2D image data, extending these to 3D environments involves unique obstacles. This paper introduces a training-free framework that leverages Vision-Language Models (VLMs) for effective OOD detection in 3D point clouds. By constructing a graph based on class prototypes and testing data, we exploit the data manifold structure to enhancing the effectiveness of VLMs for 3D OOD detection. We propose a novel Graph Score Propagation (GSP) method that incorporates prompt clustering and self-training negative prompting to improve OOD scoring with VLM. Our method is also adaptable to few-shot scenarios, providing options for practical applications. We demonstrate that GSP consistently outperforms state-of-the-art methods across synthetic and real-world datasets 3D point cloud OOD detection.
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