48 citations · 68 across the 4 of their papers we have counts for
6 papers
Efficient Prediction of Dense Visual Embeddings via Distillation and RGB-D Transformers
Söhnke Benedikt Fischedick, Daniel Seichter, Benedict Stephan +2
In domestic environments, robots require a comprehensive understanding of their surroundings to interact effectively and intuitively with untrained humans. In this paper, we propos…
PanopticNDT: Efficient and Robust Panoptic Mapping
Daniel Seichter, Benedict Stephan, Söhnke Benedikt Fischedick +3
As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate…
Efficient Multi-Task Scene Analysis with RGB-D Transformers
Söhnke Benedikt Fischedick, Daniel Seichter, Robin Schmidt +2
Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the s…
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Daniel Seichter, Söhnke Benedikt Fischedick, Mona Köhler +1
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about indi…
Efficient and Robust Semantic Mapping for Indoor Environments
Daniel Seichter, Patrick Langer, Tim Wengefeld +3
A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objec…
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
Daniel Seichter, Mona Köhler, Benjamin Lewandowski +2
Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assiste…