collaborators

7 papers

cs.CV2026

SegFly: A Dataset and 2D-3D-2D Paradigm for Aerial RGB-Thermal Semantic Segmentation at Scale

Markus Gross, Sai Bharadhwaj Matha, Rui Song +4

Semantic segmentation for uncrewed aerial vehicles (UAVs) is fundamental for aerial scene understanding, yet existing RGB and RGB-T datasets remain limited in scale, diversity, and…

cs.CV2026

GMT: Goal-Conditioned Multimodal Transformer for 6-DOF Object Trajectory Synthesis in 3D Scenes

Huajian Zeng, Abhishek Saroha, Daniel Cremers +1

Synthesizing controllable 6-DOF object manipulation trajectories in 3D environments is essential for enabling robots to interact with complex scenes, yet remains challenging due to…

cs.RO2026

SafeLand: Safe Autonomous Landing in Unknown Environments with Bayesian Semantic Mapping

Markus Gross, Andreas Greiner, Sai Bharadhwaj Matha +4

Autonomous landing of uncrewed aerial vehicles (UAVs) in unknown, dynamic environments poses significant safety challenges, particularly near people and infrastructure, as UAVs tra…

cs.CV2026

Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction

Dominik Rößle, Jeremias Gerner, Klaus Bogenberger +3

Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative percep…

cs.CV2026

UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception

Karthikeyan Chandra Sekaran, Markus Geisler, Dominik Rößle +6

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to o…

cs.CV2026

DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration

Dominik Rößle, Xujun Xie, Adithya Mohan +3

Perception is a cornerstone of autonomous driving, enabling vehicles to understand their surroundings and make safe, reliable decisions. Developing robust perception algorithms req…