1 citations · 2 across the 10 of their papers we have counts for
11 papers
CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
Franz Hanke, Antonia Bieringer, Olaf Wysocki +1
Detailed 3D building models are crucial for urban planning, digital twins, and disaster management applications. While Level of Detail 1 (LoD)1 and LoD2 building models are widely…
GS4Buildings: Prior-Guided Gaussian Splatting for 3D Building Reconstruction
Qilin Zhang, Olaf Wysocki, Boris Jutzi
Recent advances in Gaussian Splatting (GS) have demonstrated its effectiveness in photo-realistic rendering and 3D reconstruction. Among these, 2D Gaussian Splatting (2DGS) is part…
To Glue or Not to Glue? Classical vs Learned Image Matching for Mobile Mapping Cameras to Textured Semantic 3D Building Models
Simone Gaisbauer, Prabin Gyawali, Qilin Zhang +2
Feature matching is a necessary step for many computer vision and photogrammetry applications such as image registration, structure-from-motion, and visual localization. Classical…
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…