SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection

2 code implementations • 10 Apr 2024 • Mathis Kruse, Marco Rudolph, Dominik Woiwode, Bodo Rosenhahn

Detecting anomalies in images has become a well-explored problem in both academia and industry.

3D Anomaly Detection

Q-SENN: Quantized Self-Explaining Neural Networks

1 code implementation • 21 Dec 2023 • Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn

Explanations in Computer Vision are often desired, but most Deep Neural Networks can only provide saliency maps with questionable faithfulness.

Image Classification Interpretable Machine Learning

The voraus-AD Dataset for Anomaly Detection in Robot Applications

1 code implementation • 8 Nov 2023 • Jan Thieß Brockmann, Marco Rudolph, Bodo Rosenhahn, Bastian Wandt

During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production.

Anomaly Detection Benchmarking +2

Take 5: Interpretable Image Classification with a Handful of Features

1 code implementation • 23 Mar 2023 • Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn

We argue that a human can only understand the decision of a machine learning model, if the features are interpretable and only very few of them are used for a single decision.

Fine-Grained Image Classification Interpretable Machine Learning

Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure

1 code implementation • 21 Nov 2022 • Lutz M. K. Krause, Emily Manderfeld, Patricia Gnutt, Louisa Vogler, Ann Wassick, Kailey Richard, Marco Rudolph, Kelli Z. Hunsucker, Geoffrey W. Swain, Bodo Rosenhahn, Axel Rosenhahn

Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices.

Semantic Segmentation

Asymmetric Student-Teacher Networks for Industrial Anomaly Detection

1 code implementation • 14 Oct 2022 • Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, Bastian Wandt

We train a normalizing flow for density estimation as a teacher and a conventional feed-forward network as a student to trigger large distances for anomalies: The bijectivity of the normalizing flow enforces a divergence of teacher outputs for anomalies compared to normal data.

3D Anomaly Detection Defect Detection +2

Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection

1 code implementation • 6 Oct 2021 • Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, Bastian Wandt

In industrial manufacturing processes, errors frequently occur at unpredictable times and in unknown manifestations.

Defect Detection Unsupervised Anomaly Detection

Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows

1 code implementation • ICCV 2021 • Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn, Bastian Wandt

3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions.

Monocular 3D Human Pose Estimation Multi-Hypotheses 3D Human Pose Estimation

CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild

1 code implementation • CVPR 2021 • Bastian Wandt, Marco Rudolph, Petrissa Zell, Helge Rhodin, Bodo Rosenhahn

Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately.

Monocular 3D Human Pose Estimation Weakly-supervised 3D Human Pose Estimation

Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows

3 code implementations • 28 Aug 2020 • Marco Rudolph, Bastian Wandt, Bodo Rosenhahn

To achieve a high robustness and performance we exploit multiple transformations in training and evaluation.

Anomaly Detection Defect Detection +1

Structuring Autoencoders

no code implementations • 7 Aug 2019 • Marco Rudolph, Bastian Wandt, Bodo Rosenhahn

In this paper we propose Structuring AutoEncoders (SAE).