HGCF: Hierarchical Geometry-Color Fusion for Multimodal Industrial Anomaly Detection

While current multimodal anomaly detection methods predominantly employ intermediate fusion strategies, they often suffer from inadequate cross-modal interaction and irreversible information loss during feature alignment processes. To overcome these limitations, we propose Hierarchical Geometry-Color Fusion (HGCF), a novel framework that establishes deep synergistic relationships between RGB texture features and point cloud geometric representations. Firstly, we propose a bidirectional cross-modal early fusion mechanism that enables complementary information exchange between point cloud and RGB modalities at the input level. Secondly, we introduce a local self-supervised geometric color reconstruction network with group-wise feature alignment, enhancing fine-grained feature extraction through joint color-geometry reconstruction tasks. Finally, we propose a local window spatial-consistent attention fusion, which achieves semantic consistency and spatial consistency by emphasizing local mutation features to improve the detection of subtle anomalies. Extensive experiments show our model achieves 99.1% I-AUROC on MVTec 3D-AD and 91.7% on Eyecandies, both surpassing state-of-the-art methods.