跨模态映射与双分支重建用于2D-3D多模态工业异常检测
Cross-Modal Mapping and Dual-Branch Reconstruction for 2D-3D Multimodal Industrial Anomaly Detection
AI总结：
CMDR-IAD通过跨模态映射与双分支重建实现2D-3D多模态工业异常检测，具有轻量、模态灵活和高鲁棒性的特点。
摘要：
多模态工业异常检测受益于将RGB外观与3D表面几何相结合，但现有无监督方法通常依赖于内存银行、教师-学生架构或脆弱的融合方案，限制了在噪声深度、弱纹理或缺失模态下的鲁棒性。本文介绍了CMDR-IAD，一种轻量且模态灵活的无监督框架，用于可靠地检测2D+3D多模态以及单模态（2D-only或3D-only）设置中的异常。CMDR-IAD结合双向2D↔3D跨模态映射，以建模外观-几何一致性，并通过双分支重建独立捕捉正常纹理和几何结构。一种两阶段融合策略整合了这些线索：可靠性门控映射异常突出显示空间一致的纹理-几何不一致，而置信度加权重建异常自适应地平衡外观和几何偏差，即使在深度稀疏或低纹理区域也能获得稳定且精确的异常定位。在MVTec 3D-AD基准测试中，CMDR-IAD实现了最先进的性能，同时不使用内存银行，达到97.3%的图像级AUROC（I-AUROC）、99.6%的像素级AUROC（P-AUROC）和97.6%的AUPRO。在现实世界聚氨酯切割数据集上，3D-only变体实现了92.6%的I-AUROC和92.5%的P-AUROC，展示了在实际工业条件下的强大有效性。这些结果突显了该框架的鲁棒性、模态灵活性以及所提出融合策略的有效性。我们的源代码可在https://github.com/ECGAI-Research/CMDR-IAD/上获得。
英文摘要：
Multimodal industrial anomaly detection benefits from integrating RGB appearance with 3D surface geometry, yet existing \emph{unsupervised} approaches commonly rely on memory banks, teacher-student architectures, or fragile fusion schemes, limiting robustness under noisy depth, weak texture, or missing modalities. This paper introduces \textbf{CMDR-IAD}, a lightweight and modality-flexible unsupervised framework for reliable anomaly detection in 2D+3D multimodal as well as single-modality (2D-only or 3D-only) settings. \textbf{CMDR-IAD} combines bidirectional 2D$\leftrightarrow$3D cross-modal mapping to model appearance-geometry consistency with dual-branch reconstruction that independently captures normal texture and geometric structure. A two-part fusion strategy integrates these cues: a reliability-gated mapping anomaly highlights spatially consistent texture-geometry discrepancies, while a confidence-weighted reconstruction anomaly adaptively balances appearance and geometric deviations, yielding stable and precise anomaly localization even in depth-sparse or low-texture regions. On the MVTec 3D-AD benchmark, CMDR-IAD achieves state-of-the-art performance while operating without memory banks, reaching 97.3\% image-level AUROC (I-AUROC), 99.6\% pixel-level AUROC (P-AUROC), and 97.6\% AUPRO. On a real-world polyurethane cutting dataset, the 3D-only variant attains 92.6\% I-AUROC and 92.5\% P-AUROC, demonstrating strong effectiveness under practical industrial conditions. These results highlight the framework's robustness, modality flexibility, and the effectiveness of the proposed fusion strategies for industrial visual inspection. Our source code is available at https://github.com/ECGAI-Research/CMDR-IAD/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
