19 citations · 26 across the 22 of their papers we have counts for
23 papers
Modular Bayesian Inference for Partially Misspecified Mixtures with Uncertain Class Labels
Maximilian Autenrieth, Konstantin Karchev, David A. van Dyk +2
Scientific inference often involves observations from multiple latent populations whose class memberships are uncertain and must be inferred from available data. While full Bayesia…
UnderOneFacade: Worldwide Facade Semantic Segmentation Benchmark Dataset
Yi Wang, Fan Wang, Prabin Gyawali +9
Globally consistent semantic digital twins require centimeter-accurate and geographically transferable 3D facade segmentation. However, progress in facade parsing is limited by the…
ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes
Rui Song, Tianhui Cai, Markus Gross +5
Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, whi…
GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priors
Qilin Zhang, Jinyu Zhu, Olaf Wysocki +2
Recent semantic 3D Gaussian Splatting (3DGS) methods primarily rely on 2D foundation models, often yielding ambiguous boundaries and limited support for structured urban semantics.…
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…
Texture2LoD3: Enabling LoD3 Building Reconstruction With Panoramic Images
Wenzhao Tang, Weihang Li, Xiucheng Liang +4
Despite recent advancements in surface reconstruction, Level of Detail (LoD) 3 building reconstruction remains an unresolved challenge. The main issue pertains to the object-orient…
OPAL: Visibility-aware LiDAR-to-OpenStreetMap Place Recognition via Adaptive Radial Fusion
Shuhao Kang, Martin Y. Liao, Yan Xia +3
LiDAR place recognition is a critical capability for autonomous navigation and cross-modal localization in large-scale outdoor environments. Existing approaches predominantly depen…
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…
CDGS: Confidence-Aware Depth Regularization for 3D Gaussian Splatting
Qilin Zhang, Olaf Wysocki, Steffen Urban +1
3D Gaussian Splatting (3DGS) has shown significant advantages in novel view synthesis (NVS), particularly in achieving high rendering speeds and high-quality results. However, its…
ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset
Olaf Wysocki, Yue Tan, Thomas Froech +5
Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods,…
Enriching thermal point clouds of buildings using semantic 3D building models
Jingwei Zhu, Olaf Wysocki, Christoph Holst +1
Thermal point clouds integrate thermal radiation and laser point clouds effectively. However, the semantic information for the interpretation of building thermal point clouds can h…
Analyzing the impact of semantic LoD3 building models on image-based vehicle localization
Antonia Bieringer, Olaf Wysocki, Sebastian Tuttas +2
Numerous navigation applications rely on data from global navigation satellite systems (GNSS), even though their accuracy is compromised in urban areas, posing a significant challe…
Transferring facade labels between point clouds with semantic octrees while considering change detection
Sophia Schwarz, Tanja Pilz, Olaf Wysocki +2
Point clouds and high-resolution 3D data have become increasingly important in various fields, including surveying, construction, and virtual reality. However, simply having this d…
Reconstructing facade details using MLS point clouds and Bag-of-Words approach
Thomas Froech, Olaf Wysocki, Ludwig Hoegner +1
In the reconstruction of façade elements, the identification of specific object types remains challenging and is often circumvented by rectangularity assumptions or the use of boun…
Classifying point clouds at the facade-level using geometric features and deep learning networks
Yue Tan, Olaf Wysocki, Ludwig Hoegner +1
3D building models with facade details are playing an important role in many applications now. Classifying point clouds at facade-level is key to create such digital replicas of th…
MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern…
Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks
Olaf Wysocki, Yan Xia, Magdalena Wysocki +4
Reconstructing semantic 3D building models at the level of detail (LoD) 3 is a long-standing challenge. Unlike mesh-based models, they require watertight geometry and object-wise s…
Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification
Olaf Wysocki, Eleonora Grilli, Ludwig Hoegner +1
Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their ae…