Search Results for author: David Sattlegger

Found 4 papers, 3 papers with code

Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

no code implementations23 Feb 2022 Paul Bergmann, David Sattlegger

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)

3D Anomaly Detection 3D Anomaly Detection and Segmentation

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

3 code implementations CVPR 2020 Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger

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.

Unsupervised Anomaly Detection

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

13 code implementations5 Jul 2018 Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, Carsten Steger

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data.

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