Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning
This work addresses anomaly detection in 3D industrial inspection, representing an incremental improvement over existing multimodal reconstruction approaches.
The paper tackled 3D anomaly detection by proposing Mentor3AD, a method using multi-modality mentor learning to fuse RGB and 3D features for improved reconstruction, achieving enhanced detection performance as verified on MVTec 3D-AD and Eyecandies datasets.
Multimodal feature reconstruction is a promising approach for 3D anomaly detection, leveraging the complementary information from dual modalities. We further advance this paradigm by utilizing multi-modal mentor learning, which fuses intermediate features to further distinguish normal from feature differences. To address these challenges, we propose a novel method called Mentor3AD, which utilizes multi-modal mentor learning. By leveraging the shared features of different modalities, Mentor3AD can extract more effective features and guide feature reconstruction, ultimately improving detection performance. Specifically, Mentor3AD includes a Mentor of Fusion Module (MFM) that merges features extracted from RGB and 3D modalities to create a mentor feature. Additionally, we have designed a Mentor of Guidance Module (MGM) to facilitate cross-modal reconstruction, supported by the mentor feature. Lastly, we introduce a Voting Module (VM) to more accurately generate the final anomaly score. Extensive comparative and ablation studies on MVTec 3D-AD and Eyecandies have verified the effectiveness of the proposed method.