185 citations · 286 across the 34 of their papers we have counts for
39 papers
LeanStereo: A Leaner Backbone based Stereo Network
Rafia Rahim, Samuel Woerz, Andreas Zell
Recently, end-to-end deep networks based stereo matching methods, mainly because of their performance, have gained popularity. However, this improvement in performance comes at the…
Distilling Stereo Networks for Performant and Efficient Leaner Networks
Rafia Rahim, Samuel Woerz, Andreas Zell
Knowledge distillation has been quite popular in vision for tasks like classification and segmentation however not much work has been done for distilling state-of-the-art stereo ma…
Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs
Benjamin Kiefer, Timon Höfer, Andreas Zell
Yaw estimation of boats from the viewpoint of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) or boats is a crucial task in various applications such as 3D sce…
Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction
Marcel Hallgarten, Ismail Kisa, Martin Stoll +1
Predicting the future motion of observed vehicles is a crucial enabler for safe autonomous driving. The field of motion prediction has seen large progress recently with state-of-th…
SpinDOE: A ball spin estimation method for table tennis robot
Thomas Gossard, Jonas Tebbe, Andreas Ziegler +1
Spin plays a considerable role in table tennis, making a shot's trajectory harder to read and predict. However, the spin is challenging to measure because of the ball's high veloci…
Memory Maps for Video Object Detection and Tracking on UAVs
Benjamin Kiefer, Yitong Quan, Andreas Zell
This paper introduces a novel approach to video object detection detection and tracking on Unmanned Aerial Vehicles (UAVs). By incorporating metadata, the proposed approach creates…
Multiperspective Teaching of Unknown Objects via Shared-gaze-based Multimodal Human-Robot Interaction
Daniel Weber, Wolfgang Fuhl, Enkelejda Kasneci +1
For successful deployment of robots in multifaceted situations, an understanding of the robot for its environment is indispensable. With advancing performance of state-of-the-art o…
From Prediction to Planning With Goal Conditioned Lane Graph Traversals
Marcel Hallgarten, Martin Stoll, Andreas Zell
The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models…
Fast Region of Interest Proposals on Maritime UAVs
Benjamin Kiefer, Andreas Zell
Unmanned aerial vehicles assist in maritime search and rescue missions by flying over large search areas to autonomously search for objects or people. Reliably detecting objects of…
Wavelength-aware 2D Convolutions for Hyperspectral Imaging
Leon Amadeus Varga, Martin Messmer, Nuri Benbarka +1
Deep Learning could drastically boost the classification accuracy for Hyperspectral Imaging (HSI). Still, the training on the mostly small hyperspectral data sets is not trivial. T…
Real-time event simulation with frame-based cameras
Andreas Ziegler, Daniel Teigland, Jonas Tebbe +2
Event cameras are becoming increasingly popular in robotics and computer vision due to their beneficial properties, e.g., high temporal resolution, high bandwidth, almost no motion…
Gaze-based Object Detection in the Wild
Daniel Weber, Wolfgang Fuhl, Andreas Zell +1
In human-robot collaboration, one challenging task is to teach a robot new yet unknown objects enabling it to interact with them. Thereby, gaze can contain valuable information. We…
Comprehensive Analysis of the Object Detection Pipeline on UAVs
Leon Amadeus Varga, Sebastian Koch, Andreas Zell
An object detection pipeline comprises a camera that captures the scene and an object detector that processes these images. The quality of the images directly affects the performan…
Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles
Benjamin Kiefer, David Ott, Andreas Zell
Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environme…
FourierMask: Instance Segmentation using Fourier Mapping in Implicit Neural Networks
Hamd ul Moqeet Riaz, Nuri Benbarka, Timon Hoefer +1
We present FourierMask, which employs Fourier series combined with implicit neural representations to generate instance segmentation masks. We apply a Fourier mapping (FM) to the c…
Optimal Stroke Learning with Policy Gradient Approach for Robotic Table Tennis
Yapeng Gao, Jonas Tebbe, Andreas Zell
Learning to play table tennis is a challenging task for robots, as a wide variety of strokes required. Recent advances have shown that deep Reinforcement Learning (RL) is able to s…
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation
Faranak Shamsafar, Andreas Zell
Stereo vision is an effective technique for depth estimation with broad applicability in autonomous urban and highway driving. While various deep learning-based approaches have bee…
Seeing Implicit Neural Representations as Fourier Series
Nuri Benbarka, Timon Höfer, Hamd ul-moqeet Riaz +1
Implicit Neural Representations (INR) use multilayer perceptrons to represent high-frequency functions in low-dimensional problem domains. Recently these representations achieved s…
Separable Convolutions for Optimizing 3D Stereo Networks
Rafia Rahim, Faranak Shamsafar, Andreas Zell
Deep learning based 3D stereo networks give superior performance compared to 2D networks and conventional stereo methods. However, this improvement in the performance comes at the…
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
Faranak Shamsafar, Samuel Woerz, Rafia Rahim +1
Recent methods in stereo matching have continuously improved the accuracy using deep models. This gain, however, is attained with a high increase in computation cost, such that the…
Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training
Maximus Mutschler, Kevin Laube, Andreas Zell
A fundamental challenge in Deep Learning is to find optimal step sizes for stochastic gradient descent automatically. In traditional optimization, line searches are a commonly used…
Score refinement for confidence-based 3D multi-object tracking
Nuri Benbarka, Jona Schröder, Andreas Zell
Multi-object tracking is a critical component in autonomous navigation, as it provides valuable information for decision-making. Many researchers tackled the 3D multi-object tracki…
Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking
Timon Höfer, Faranak Shamsafar, Nuri Benbarka +1
Bin picking is a core problem in industrial environments and robotics, with its main module as 6D pose estimation. However, industrial depth sensors have a lack of accuracy when it…
Tackling the Background Bias in Sparse Object Detection via Cropped Windows
Leon Amadeus Varga, Andreas Zell
Object detection on Unmanned Aerial Vehicles (UAVs) is still a challenging task. The recordings are mostly sparse and contain only small objects. In this work, we propose a simple…