12 citations · 21 across the 10 of their papers we have counts for
12 papers
Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery
Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn +1
Monocular 3D human pose and shape estimation is an inherently ill-posed problem due to depth ambiguities, occlusions, and truncations. Recent probabilistic approaches learn a distr…
SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection
Mathis Kruse, Marco Rudolph, Dominik Woiwode +1
Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult set…
Q-SENN: Quantized Self-Explaining Neural Networks
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. Self-Explaining Neural Networks (SEN…
The voraus-AD Dataset for Anomaly Detection in Robot Applications
Jan Thieß Brockmann, Marco Rudolph, Bodo Rosenhahn +1
During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensu…
Take 5: Interpretable Image Classification with a Handful of Features
Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn
Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimens…
Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure
Lutz M. K. Krause, Emily Manderfeld, Patricia Gnutt +8
Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluat…
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn +1
Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work disc…
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn +1
In industrial manufacturing processes, errors frequently occur at unpredictable times and in unknown manifestations. We tackle the problem of automatic defect detection without req…
Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn +1
3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions. Nonetheless, most existing works ignore these ambiguities and…
CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild
Bastian Wandt, Marco Rudolph, Petrissa Zell +2
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. Unfortunately, fo…
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
Marco Rudolph, Bastian Wandt, Bodo Rosenhahn
The detection of manufacturing errors is crucial in fabrication processes to ensure product quality and safety standards. Since many defects occur very rarely and their characteris…
Structuring Autoencoders
Marco Rudolph, Bastian Wandt, Bodo Rosenhahn
In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural networks which learn a low dimensional representation of data which are additionally enriched with a desire…