Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning

TL;DR

Mentor3AD introduces a multi-modal mentor learning framework for 3D anomaly detection, combining RGB and 3D features to enhance reconstruction and detection accuracy through novel modules and extensive validation.

Contribution

This work presents a new multi-modal mentor learning approach with fusion, guidance, and voting modules for improved 3D anomaly detection performance.

Findings

Outperforms existing methods on MVTec 3D-AD and Eyecandies datasets.

Effective feature fusion and guidance improve anomaly detection accuracy.

Ablation studies confirm the contribution of each module.

Abstract

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.