76 citations · 205 across the 15 of their papers we have counts for
11 papers · 1 filter
Online Out-of-Domain Detection for Automated Driving
Timo Sämann, Horst-Michael Groß
Ensuring safety in automated driving is a major challenge for the automotive industry. Special attention is paid to artificial intelligence, in particular to Deep Neural Networks (…
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly
Dustin Aganian, Mona Köhler, Benedict Stephan +2
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize hu…
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks
Dustin Aganian, Mona Köhler, Sebastian Baake +2
As the use of collaborative robots (cobots) in industrial manufacturing continues to grow, human action recognition for effective human-robot collaboration becomes increasingly imp…
Efficient Multi-Task Scene Analysis with RGB-D Transformers
Söhnke Benedikt Fischedick, Daniel Seichter, Robin Schmidt +2
Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the s…
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Daniel Seichter, Söhnke Benedikt Fischedick, Mona Köhler +1
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about indi…
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Timo Sämann, Ahmed Mostafa Hammam, Andrei Bursuc +2
Averaging predictions of a deep ensemble of networks is apopular and effective method to improve predictive performance andcalibration in various benchmarks and Kaggle competitions…
Few-Shot Object Detection: A Comprehensive Survey
Mona Köhler, Markus Eisenbach, Horst-Michael Gross
Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To a…
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
Daniel Seichter, Mona Köhler, Benjamin Lewandowski +2
Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assiste…
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
Kai Fischer, Martin Simon, Florian Oelsner +3
Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initi…
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Martin Simon, Karl Amende, Andrea Kraus +5
Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robot…
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Martin Simon, Stefan Milz, Karl Amende +1
Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and moti…