  Recent studies of multi-modal Industrial Anomaly Detection (IAD) based on
point clouds and RGB images indicated the importance of exploiting redundancy
and complementarity among modalities for accurate classification and
segmentation. However, achieving multi-modal IAD in practical production lines
remains a work in progress that requires consideration of the trade-offs
between costs and benefits associated with introducing new modalities, while
ensuring compatibility with current processes. Combining fast in-line
inspections with high-resolution, time-consuming, near-line characterization
techniques to enhance detection accuracy fits well into the existing quality
control process, but only part of the samples can be tested with expensive
near-line methods. Thus, the model must have the ability to leverage
multi-modal training and handle incomplete modalities during inference. One
solution is generating cross-modal hallucination to transfer knowledge among
modalities for missing modality issues. In this paper, we propose CMDIAD, a
Cross-Modal Distillation framework for IAD to demonstrate the feasibility of
Multi-modal Training, Few-modal Inference pipeline. Moreover, we investigate
reasons behind the asymmetric performance improvement using point clouds or RGB
images as main modality of inference. This lays the foundation of our future
multi-modal dataset construction for efficient IAD from manufacturing
scenarios.
