当点云优于像素：重新思考零样本多模态异常检测

中文摘要 AI 辅助

零样本多模态异常检测通常假设RGB和点云模态具有同等的可靠性，并能均匀地贡献于异常定位。我们挑战了这一假设。通过使用一组近期提出的严格指标（这些指标会惩罚正常区域中的虚假异常响应），我们发现点云在零样本类别偏移下比RGB更可靠。基于这一观察，我们提出了WOOPS（当点云优于像素时），一个可靠性感知的零样本多模态异常检测框架。为了增强更可靠的几何模态，我们设计了一个多视角信息解耦模块，以抑制多视角点云投影中的异构信息并提升点云特征质量。为了避免无条件融合，我们进一步引入了一个模态可靠性校准模块，根据模态的可靠性自适应地校准其贡献。大量实验表明，我们的方法在新指标下，在单模态和多模态设置中均取得了最佳或具有竞争力的性能。进一步的分析表明，点云信息也改善了仅使用RGB的推理，而消融实验验证了两个模块的有效性。代码将在论文被接收后发布。

英文摘要

Zero-shot multimodal anomaly detection commonly assumes that RGB and point cloud modalities are equally reliable and can contribute uniformly to anomaly localization. We challenge this assumption. Using a set of recently proposed stringent metrics that penalize false anomaly responses in normal regions, we find that point clouds are substantially more reliable than RGB under zero-shot category shift. Motivated by this observation, we propose WOOPS (\textbf{W}hen P\textbf{o}int Cl\textbf{o}uds Out\textbf{p}erform Pixel\textbf{s}), a reliability-aware zero-shot multimodal anomaly detection framework. To strengthen the more reliable geometric modality, we design a Multi-view Information Decoupling module to suppress heterogeneous information from multi-view point cloud projections and enhance point cloud feature quality. To avoid unconditional fusion, we further introduce a Modality Reliability Calibration module to adaptively calibrate modality contributions according to their reliability. Extensive experiments show that our method achieves the best or competitive performance under the new metrics in both unimodal and multimodal settings. Further analysis demonstrates that point cloud information also improves RGB-only inference, while ablations verify the effectiveness of both modules. Code will be released upon acceptance.

发表机构

• School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）

机构由 AI 辅助整理，请以论文原文为准。