10 citations · 26 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Steven Landgraf, Markus Hillemann, Theodor Kapler +1
Quantifying the predictive uncertainty emerged as a possible solution to common challenges like overconfidence or lack of explainability and robustness of deep neural networks, alb…