4 citations · 8 across the 17 of their papers we have counts for
18 papers
Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language
Julian Truetsch, Felix Hauser, Christoph Stiller +1
Understanding the composition of large-scale autonomous driving datasets is essential for safety, robustness, and reliable operation across domains. For example, domain shift betwe…
Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation
Kaiwen Wang, Frank Bieder, Yinzhe Shen +3
Simulation-to-reality translation must bridge the appearance gap between synthetic and real domains while preserving structural and semantic consistency. Conditioning-based methods…
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
Richard Schwarzkopf, Jonas Merkert, Frank Bieder +22
Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategi…
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
Richard Schwarzkopf, Fabian Immel, Alexander Blumberg +21
Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a E…
Impact of Localization Errors on Label Quality for Online HD Map Construction
Alexander Blumberg, Jonas Merkert, Richard Fehler +4
High-definition (HD) maps are crucial for autonomous vehicles, but their creation and maintenance is very costly. This motivates the idea of online HD map construction. To provide…
XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation
Frank Bieder, Hendrik Königshof, Haohao Hu +4
Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor-specific data availability. To bridge…
SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
Fabian Immel, Jan-Hendrik Pauls, Richard Fehler +3
Autonomous vehicles rely on detailed and accurate environmental information to operate safely. High definition (HD) maps offer a promising solution, but their high maintenance cost…
M3TR: A Generalist Model for Real-World HD Map Completion
Fabian Immel, Richard Fehler, Frank Bieder +2
Autonomous vehicles rely on HD maps for their operation, but offline HD maps eventually become outdated. For this reason, online HD map construction methods use live sensor data to…
Generation of Training Data from HD Maps in the Lanelet2 Framework
Fabian Immel, Richard Fehler, Frank Bieder +1
Using HD maps directly as training data for machine learning tasks has seen a massive surge in popularity and shown promising results, e.g. in the field of map perception. Despite…
Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles
Sven Richter, Frank Bieder, Sascha Wirges +1
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell…
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution
Sven Richter, Frank Bieder, Sascha Wirges +1
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources. Dempster's combination rule that is usually applied in this contex…
Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
Sascha Wirges, Kevin Rösch, Frank Bieder +1
We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle. The ground surface is modeled as a UBS which is robust towards v…
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations
Frank Bieder, Maximilian Link, Simon Romanski +2
In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse lear…
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function
Haohao Hu, Hexing Yang, Jian Wu +4
The Large-scale 3D reconstruction, texturing and semantic mapping are nowadays widely used for automated driving vehicles, virtual reality and automatic data generation. However, m…
TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis
Haohao Hu, Fengze Han, Frank Bieder +2
In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environmen…
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
Kunyu Peng, Juncong Fei, Kailun Yang +6
At the heart of all automated driving systems is the ability to sense the surroundings, e.g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progr…
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data
Juncong Fei, Kunyu Peng, Philipp Heidenreich +2
Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic s…
Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data
Frank Bieder, Sascha Wirges, Johannes Janosovits +3
In this paper, we consider the transformation of laser range measurements into a top-view grid map representation to approach the task of LiDAR-only semantic segmentation. Since th…