  The paper explores the industrial multimodal Anomaly Detection (AD) task,
which exploits point clouds and RGB images to localize anomalies. We introduce
a novel light and fast framework that learns to map features from one modality
to the other on nominal samples. At test time, anomalies are detected by
pinpointing inconsistencies between observed and mapped features. Extensive
experiments show that our approach achieves state-of-the-art detection and
segmentation performance in both the standard and few-shot settings on the
MVTec 3D-AD dataset while achieving faster inference and occupying less memory
than previous multimodal AD methods. Moreover, we propose a layer-pruning
technique to improve memory and time efficiency with a marginal sacrifice in
performance.
