39 citations · 39 across the 1 of their papers we have counts for
6 papers · 1 filter
Self-Supervised Moving Object Segmentation of Sparse and Noisy Radar Point Clouds
Leon Schwarzer, Matthias Zeller, Daniel Casado Herraez +3
Moving object segmentation is a crucial task for safe and reliable autonomous mobile systems like self-driving cars, improving the reliability and robustness of subsequent tasks li…
SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds
Matthias Zeller, Daniel Casado Herraez, Bengisu Ayan +3
Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Came…
Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds
Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch +3
The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The interpretation of LiDAR and camera da…
Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds
Matthias Zeller, Daniel Casado Herraez, Jens Behley +2
Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, incl…
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds
Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch +3
The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretati…
Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data
Matthias Zeller, Jens Behley, Michael Heidingsfeld +1
Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perceptio…