3 papers
cs.CV2025
A Data-Centric Approach to 3D Semantic Segmentation of Railway Scenes
Nicolas Münger, Max Peter Ronecker, Xavier Diaz +3
LiDAR-based semantic segmentation is critical for autonomous trains, requiring accurate predictions across varying distances. This paper introduces two targeted data augmentation m…
cs.CV2025
LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring
Raul David Dominguez Sanchez, Xavier Diaz Ortiz, Xingcheng Zhou +4
Railway systems, particularly in Germany, require high levels of automation to address legacy infrastructure challenges and increase train traffic safely. A key component of automa…
cs.RO2024
Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping
Max Peter Ronecker, Xavier Diaz, Michael Karner +1
This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-class…