2 citations · 5 across the 17 of their papers we have counts for
19 papers
A Multi-Source Ultrasound Benchmark Revealing the Limits of Contemporary Self-Supervised Anomaly Detection Methods
Marco Riedenauer, Daniel Kienzle, Pratik Mayekar +1
Self-supervised anomaly detection is a promising paradigm for medical ultrasound, as normal images are often easier to obtain than exhaustive annotations of all possible pathologie…
Event Detection in Table Tennis Videos using 2D Keypoints
Rainer Lienhart, Daniel Kienzle, Shin'ichi Satoh +1
This paper addresses the challenge of automatic, frame-accurate event detection in table tennis videos. Current methods for estimating 3d ball trajectories and ball spin typically…
Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda +3
Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets. This combination yields strong ben…
TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos
Nima Rahmanian, Daniel Kienzle, Thomas Gossard +3
We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featurin…
DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime
Julian Lorenz, Vladyslav Kovganko, Elias Kohout +3
Scene Graph Generation (SGG) aims to extract a detailed graph structure from an image, a representation that holds significant promise as a robust intermediate step for complex dow…
Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation
Daniel Kienzle, Katja Ludwig, Julian Lorenz +2
Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize…
Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer
Daniel Kienzle, Robin Schön, Rainer Lienhart +1
Analyzing a player's technique in table tennis requires knowledge of the ball's 3D trajectory and spin. While, the spin is not directly observable in standard broadcasting videos,…
Efficient 2D to Full 3D Human Pose Uplifting including Joint Rotations
Katja Ludwig, Yuliia Oksymets, Robin Schön +2
In sports analytics, accurately capturing both the 3D locations and rotations of body joints is essential for understanding an athlete's biomechanics. While Human Mesh Recovery (HM…
SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts
Robin Schön, Julian Lorenz, Daniel Kienzle +1
In this paper, we present a novel architecture for interactive segmentation in winter sports contexts. The field of interactive segmentation deals with the prediction of high-quali…
Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes
Katja Ludwig, Julian Lorenz, Daniel Kienzle +2
The basic body shape (i.e., the body shape in T-pose) of a person does not change within a single video. However, most SOTA human mesh estimation (HME) models output a slightly dif…
WSESeg: Introducing a Dataset for the Segmentation of Winter Sports Equipment with a Baseline for Interactive Segmentation
Robin Schön, Daniel Kienzle, Rainer Lienhart
In this paper we introduce a new dataset containing instance segmentation masks for ten different categories of winter sports equipment, called WSESeg (Winter Sports Equipment Segm…
A Fair Ranking and New Model for Panoptic Scene Graph Generation
Julian Lorenz, Alexander Pest, Daniel Kienzle +2
In panoptic scene graph generation (PSGG), models retrieve interactions between objects in an image which are grounded by panoptic segmentation masks. Previous evaluations on panop…
Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
Daniel Kienzle, Marco Kantonis, Robin Schön +1
Utilizing transformer architectures for semantic segmentation of high-resolution images is hindered by the attention's quadratic computational complexity in the number of tokens. A…
Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
Daniel Kienzle, Julian Lorenz, Katja Ludwig +1
We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead…
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes
Julian Lorenz, Florian Barthel, Daniel Kienzle +1
Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
Detecting Arbitrary Keypoints on Limbs and Skis with Sparse Partly Correct Segmentation Masks
Katja Ludwig, Daniel Kienzle, Julian Lorenz +1
Analyses based on the body posture are crucial for top-class athletes in many sports disciplines. If at all, coaches label only the most important keypoints, since manual annotatio…
COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings
Daniel Kienzle, Julian Lorenz, Robin Schön +2
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severi…
Recognition of Freely Selected Keypoints on Human Limbs
Katja Ludwig, Daniel Kienzle, Rainer Lienhart
Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points ar…