2.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Center for Integrated Quantum Science and TechnologyDE303 papers
- Centre National de la Recherche ScientifiqueFR82 papers
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DE71 papers
- Technische Hochschule UlmDE65 papers
- Helmholtz-Institute UlmDE64 papers
- Imperial College LondonGB58 papers
- Hebrew University of JerusalemIL52 papers
- Leibniz University HannoverDE51 papers
- University of StuttgartDE45 papers
- Texas A&M UniversityUS41 papers
- Universität InnsbruckAT40 papers
- Technical University of MunichDE37 papers
48 papers · 1 filter
MGNiceNet: Unified Monocular Geometric Scene Understanding
Markus Schön, Michael Buchholz, Klaus Dietmayer
Monocular geometric scene understanding combines panoptic segmentation and self-supervised depth estimation, focusing on real-time application in autonomous vehicles. We introduce…
Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Adverse weather conditions can severely affect the performance of LiDAR sensors by introducing unwanted noise in the measurements. Therefore, differentiating between noise and vali…
The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data
Florian Kraus, Nicolas Scheiner, Werner Ritter +1
Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasone…
Automatic Bat Call Classification using Transformer Networks
Frank Fundel, Daniel A. Braun, Sebastian Gottwald
Automatically identifying bat species from their echolocation calls is a difficult but important task for monitoring bats and the ecosystem they live in. Major challenges in automa…
Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design
Dominik Engel, Leon Sick, Timo Ropinski
In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or…
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning mod…
Energy-based Detection of Adverse Weather Effects in LiDAR Data
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliabili…
RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation
Markus Schön, Michael Buchholz, Klaus Dietmayer
Panoptic segmentation is one of the most challenging scene parsing tasks, combining the tasks of semantic segmentation and instance segmentation. While much progress has been made,…
Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles
Adrian Holzbock, Nicolai Kern, Christian Waldschmidt +2
We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic. Initially, we process the radar data with…
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht +5
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to enco…
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability
Mohammadreza Amirian, Friedhelm Schwenker
Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning application…
Gradient-based Uncertainty for Monocular Depth Estimation
Julia Hornauer, Vasileios Belagiannis
In monocular depth estimation, disturbances in the image context, like moving objects or reflecting materials, can easily lead to erroneous predictions. For that reason, uncertaint…
Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds
Aldi Piroli, Vinzenz Dallabetta, Marc Walessa +3
LiDAR sensors used in autonomous driving applications are negatively affected by adverse weather conditions. One common, but understudied effect, is the condensation of vehicle gas…
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
Markus Schön, Michael Buchholz, Klaus Dietmayer
We introduce MGNet, a multi-task framework for monocular geometric scene understanding. We define monocular geometric scene understanding as the combination of two known tasks: Pan…
Monocular Depth Decomposition of Semi-Transparent Volume Renderings
Dominik Engel, Sebastian Hartwig, Timo Ropinski
Neural networks have shown great success in extracting geometric information from color images. Especially, monocular depth estimation networks are increasingly reliable in real-wo…
Transformers for Multi-Object Tracking on Point Clouds
Felicia Ruppel, Florian Faion, Claudius Gläser +1
We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and…
Robust 3D Object Detection in Cold Weather Conditions
Aldi Piroli, Vinzenz Dallabetta, Marc Walessa +3
Adverse weather conditions can negatively affect LiDAR-based object detectors. In this work, we focus on the phenomenon of vehicle gas exhaust condensation in cold weather conditio…
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Adrian Holzbock, Alexander Tsaregorodtsev, Youssef Dawoud +2
Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, key…
Neural Relightable Participating Media Rendering
Quan Zheng, Gurprit Singh, Hans-Peter Seidel
Learning neural radiance fields of a scene has recently allowed realistic novel view synthesis of the scene, but they are limited to synthesize images under the original fixed ligh…
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware
Julia Hornauer, Lazaros Nalpantidis, Vasileios Belagiannis
Real-world perception systems in many cases build on hardware with limited resources to adhere to cost and power limitations of their carrying system. Deploying deep neural network…
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
Markus Horn, Ole Schumann, Markus Hahn +2
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like ca…
Measuring Model Biases in the Absence of Ground Truth
Osman Aka, Ken Burke, Alex Bäuerle +2
The measurement of bias in machine learning often focuses on model performance across identity subgroups (such as man and woman) with respect to groundtruth labels. However, these…
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
Di Feng, Zining Wang, Yiyang Zhou +5
The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity…
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Di Feng, Ali Harakeh, Steven Waslander +1
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and ma…