6 papers
TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understanding
Duc Nguyen, Yan-Ling Lai, Qilin Zhang +4
3D semantic scene understanding remains a long-standing challenge in the 3D computer vision community. One of the key issues pertains to limited real-world annotated data to facili…
L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models
Ziyang Xu, Benedikt Schwab, Yihui Yang +2
Accurate registration between LiDAR (Light Detection and Ranging) point clouds and semantic 3D city models is a fundamental topic in urban digital twinning and a prerequisite for d…
Mind the Domain Gap: Measuring the Domain Gap Between Real-World and Synthetic Point Clouds for Automated Driving Development
Nguyen Duc, Yan-Ling Lai, Patrick Madlindl +5
Owing to the typical long-tail data distribution issues, simulating domain-gap-free synthetic data is crucial in robotics, photogrammetry, and computer vision research. The fundame…
TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset
Olaf Wysocki, Benedikt Schwab, Manoj Kumar Biswanath +31
Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple…
RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning
Yuan Luo, Rudolf Hoffmann, Yan Xia +4
Semantic 3D city models are worldwide easy-accessible, providing accurate, object-oriented, and semantic-rich 3D priors. To date, their potential to mitigate the noise impact on ra…
FacaDiffy: Inpainting Unseen Facade Parts Using Diffusion Models
Thomas Froech, Olaf Wysocki, Yan Xia +4
High-detail semantic 3D building models are frequently utilized in robotics, geoinformatics, and computer vision. One key aspect of creating such models is employing 2D conflict ma…