Search Results for author: David Sattlegger

Found 5 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 #6 on 3D Anomaly Detection and Segmentation on MVTEC 3D-AD (using extra training data)

3D Anomaly Detection 3D Anomaly Detection and Segmentation

Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization

no code implementations IJCV 2022 Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger

The first one inspects confined regions independent of their spatial locations in the input image and is primarily responsible for the detection of entirely new local structures.

Unsupervised Anomaly Detection

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.

Ranked #12 on Anomaly Detection on MVTec AD (Segmentation AP metric)

Descriptive Segmentation +1

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

14 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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