An augmented reality (AR) system includes: convolutional neural networks (CNNs) 500, and visualization device 520 acquiring 2-D RGB images. The system generates anchor vector 550 in a semantic space from a two-dimensional 2D RGB anchor image provided to a first RGB CNN 519. The system generates a positive vector and negative vector 560, 570 in the semantic space from a negative and positive image provided to a second normalization CNN 530. The negative and positive images are normal map images from prestored three-dimensional 3D CAD models. The system applies a cross-domain deep metric learning algorithm to extract image features in the semantic space using the anchor, positive, and negative vector, e.g. by selecting smallest distance vectors. The cross‑domain algorithm is a triplet loss algorithm. The second CNN is a Siamese network. CNNs 500 employ a skip‑connection architecture. Recognition by analysing the extracted features in the semantic space allows determining whether an invalid repair sequence occurred. For example, a repair step requires a part to be detected and the two-branch CNNs 500 are trained to detect whether the part is present or absent by comparing with normal map images including the part and not including the part.
Cross-domain metric learning system and method