融合颜色与几何信息，实现零样本工业3D异常检测
CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection
- 构建多视角对齐图像，融合2D颜色与3D几何特征
- 在MVTec3D-AD和Eyecandies上达到顶尖性能
- 适合缺乏标注数据的工业缺陷检测场景
零样本3D异常检测在工业质量检测中至关重要，因异常样本标注稀缺。现有方法缺乏有效机制融合互补的2D颜色图像与3D几何结构，限制了统一框架下对表面与结构缺陷的检测能力。为此，我们提出CoGeoAD，一种基于CLIP的统一框架，通过构建像素对齐的多视角图像，融合颜色与几何特征。该框架引入数据驱动的多视图注意力（MVA）机制，自适应聚合3D特征，并设计多阶段颜色-几何融合（MS-CGF）模块，分层整合双模态多层次特征。在MVTec3D-AD与Eyecandies基准上的大量实验表明，CoGeoAD在复杂工业场景中有效捕捉结构与纹理异常，性能达到当前最优。代码已开源：https://github.com/kingdomShu/CoGeoAD。
原文摘要 · Abstract (English)
Zero-shot 3D anomaly detection is essential for industrial quality inspection, where labeled anomaly samples are scarce. Meanwhile, existing methods lack an effective mechanism to fuse complementary 2D color images with 3D geometric structures, limiting their ability to detect both surface and structural defects in a unified framework. To address these issues, we propose CoGeoAD, a unified CLIP-based framework that fuses color and geometric features by constructing pixel-aligned paired multi-view images. The framework introduces a Data-Driven Multi-View Attention (MVA) mechanism to adaptively aggregate 3D features and a Multi-Stage Color-Geometric Fusion (MS-CGF) module to hierarchically integrate multi-level features from both modalities. Extensive experiments on the MVTec3D-AD and Eyecandies benchmarks demonstrate that CoGeoAD achieves state-of-the-art performance, effectively capturing both structural and textural anomalies in complex industrial scenarios. our source code is available at https://github.com/kingdomShu/CoGeoAD.
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