Modality-Resilient Multimodal Industrial Anomaly Detection via Cross-Modal Knowledge Transfer and Dynamic Edge-Preserving Voxelization

TL;DR

This paper introduces a robust method for detecting anomalies in industrial settings using multimodal data, even when some sensors fail or data is incomplete.

Contribution

The novel approach combines cross-modal knowledge transfer and dynamic voxelization to handle both intra-modal and cross-modal data incompleteness in industrial anomaly detection.

Findings

The proposed method achieves state-of-the-art performance in detecting anomalies with incomplete sensor data.

The approach maintains high accuracy even when one modality (RGB or 3D) is entirely missing during inference.

Dynamic edge-preserving voxelization improves computational efficiency without sacrificing geometric feature preservation.

Abstract

Achieving high-precision anomaly detection with incomplete sensor data is a critical challenge in industrial automation and intelligent manufacturing. This incompleteness often results from sensor failures, environmental interference, occlusions, or acquisition cost constraints. This study explicitly targets both types of incompleteness commonly encountered in industrial multimodal inspection: (i) incomplete sensor data within a given modality, such as partial point cloud loss or image degradation, and (ii) incomplete modalities, where one sensing channel (RGB or 3D) is entirely unavailable. By jointly addressing intra-modal incompleteness and cross-modal absence within a unified cross-distillation framework, our approach enhances anomaly detection robustness under both conditions. First, a teacher–student cross-modal distillation mechanism enables robust feature learning from both RGB and 3D modalities, allowing the student network to accurately detect anomalies even when a modality is missing during inference. Second, a dynamic voxel resolution adjustment with edge-retention strategy alleviates the computational burden of 3D point cloud processing while preserving crucial geometric features. By jointly enhancing robustness to missing modalities and improving computational efficiency, our method offers a resilient and practical solution for anomaly detection in real-world manufacturing scenarios. Extensive experiments demonstrate that the proposed method achieves both high robustness and efficiency across multiple industrial scenarios, establishing new state-of-the-art performance that surpasses existing approaches in both accuracy and speed. This method provides a robust solution for high-precision perception under complex detection conditions, significantly enhancing the feasibility of deploying anomaly detection systems in real industrial environments.

Figures