Incremental Template Neighborhood Matching for 3D anomaly detection

The unsupervised 3D industrial anomaly detection is gaining increasing attention in the computer vision community. Most advanced multimodal methods obtain anomaly detection models by one-time training, ignoring the continuous generation of new samples in industrial scenarios. In this paper, we propose Incremental Template Neighborhood Matching (ITNM), a method that enables the model to be incrementally updated by incorporating new samples, simultaneously achieving better anomaly detection and localization performance. We use a weighted feature concatenation to combine the RGB information with the point cloud information to avoid the bias caused by the difference of feature dimension and magnitude. In training stage, Pixel-wise Coreset Selection (PCS) is used to compress the multimodal template set to prevent excessive memory usage. PCS preserves the distribution of nominal sample features as much as possible, while retaining the pixel position information. In inference stage, the query feature is compared to the ones within the pixel's neighborhood in the template set. The distance between the query feature and the most similar template feature is calculated as the anomaly score. Template Neighborhood Matching (TNM) avoids underestimation of anomaly scores due to misaligned template matching of anomaly features. Our method achieves competitive performance on the MVTec 3D-AD dataset, when a low false positive rate is required. More importantly, the results of the incremental training experiments show that the incremental updating process is effective.