5 papers · 1 filter
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…
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
Markus Hillemann, Robert Langendörfer, Max Heiken +6
Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene r…
HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2
Miriam Jäger, Theodor Kapler, Michael Feßenbecker +3
In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D…
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation
Kira Wursthorn, Markus Hillemann, Markus Ulrich
The estimation of 6D object poses is a fundamental task in many computer vision applications. Particularly, in high risk scenarios such as human-robot interaction, industrial inspe…