A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds.
Ranked #2 on 3D Anomaly Detection and Segmentation on MVTEC 3D-AD (using extra training data)
We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization.
Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.
Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data.