3 citations · 3 across the 4 of their papers we have counts for
4 papers
Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
Yiting Wang, Nolwenn Peyratout, Tim Brodermann +13
This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted 17 registered participants…
AURORA-KITTI: Any-Weather Depth Completion and Denoising
Yiting Wang, Tim Brödermann, Hamed Haghighi +4
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB--LiDAR fusion methods degrade significantly under adverse weather, where both camera i…
Exploring Generative AI for Sim2Real in Driving Data Synthesis
Haonan Zhao, Yiting Wang, Thomas Bashford-Rogers +2
Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving…
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving
Yiting Wang, Haonan Zhao, Daniel Gummadi +3
Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique…