45 citations · 168 across the 10 of their papers we have counts for
10 papers
ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models
Jiarong Wei, Niclas Vödisch, Anna Rehr +2
Automated parking is a critical feature of Advanced Driver Assistance Systems (ADAS), where accurate trajectory prediction is essential to bridge perception and planning modules. D…
Collaborative Dynamic 3D Scene Graphs for Open-Vocabulary Urban Scene Understanding
Tim Steinke, Martin Büchner, Niclas Vödisch +1
Mapping and scene representation are fundamental to reliable planning and navigation in mobile robots. While purely geometric maps using voxel grids allow for general navigation, o…
Label-Efficient LiDAR Panoptic Segmentation
Ahmet Selim Çanakçı, Niclas Vödisch, Kürsat Petek +2
A main bottleneck of learning-based robotic scene understanding methods is the heavy reliance on extensive annotated training data, which often limits their generalization ability.…
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation
Niclas Vödisch, Kürsat Petek, Markus Käppeler +2
A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achie…
Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences
Kürsat Petek, Niclas Vödisch, Johannes Meyer +3
Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highl…
Few-Shot Panoptic Segmentation With Foundation Models
Markus Käppeler, Kürsat Petek, Niclas Vödisch +2
Current state-of-the-art methods for panoptic segmentation require an immense amount of annotated training data that is both arduous and expensive to obtain posing a significant ch…
Collaborative Dynamic 3D Scene Graphs for Automated Driving
Elias Greve, Martin Büchner, Niclas Vödisch +2
Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building a…
PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention
José Arce, Niclas Vödisch, Daniele Cattaneo +2
A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based met…
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard +1
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous L…
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
Niclas Vödisch, Ozan Unal, Ke Li +2
Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distrib…