6 papers
MVM-IOD: An Industrial Object-Centric Benchmark Dataset for the Evaluation of 3D Reconstruction Methods
Robert Langendörfer, Markus Hillemann, Markus Ulrich
3D object reconstruction, and camera pose estimation in industrial applications are challenging tasks, as errors are costly while the computation time is often limited. The complex…
Uncertainty Quality of VGGT: An Analysis on the DTU Benchmark Dataset
Markus Hillemann, Robert Langendörfer, Steven Landgraf +1
Visual Geometry Grounded Transformer (VGGT) has already attracted a great deal of attention in a short period of time, not least due to the Best Paper Award at CVPR-2025. Similar t…
MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances
Robert Langendörfer, Markus Hillemann, Markus Ulrich
Simultaneous 3D reconstruction and 6D object pose estimation from a single monocular image is an inherently ill-posed problem. In industrial settings, however, multiple instances o…
Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness
Steven Landgraf, Markus Hillemann, Markus Ulrich
Semantic segmentation is critical for scene understanding but demands costly pixel-wise annotations, attracting increasing attention to semi-supervised approaches to leverage abund…
FeatureGS: Eigenvalue-Feature Optimization in 3D Gaussian Splatting for Geometrically Accurate and Artifact-Reduced Reconstruction
Miriam Jäger, Markus Hillemann, Boris Jutzi
3D Gaussian Splatting (3DGS) has emerged as a powerful approach for 3D scene reconstruction using 3D Gaussians. However, neither the centers nor surfaces of the Gaussians are accur…
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation
Steven Landgraf, Markus Hillemann, Theodor Kapler +1
Deep neural networks excel in perception tasks such as semantic segmentation and monocular depth estimation, making them indispensable in safety-critical applications like autonomo…