40 citations · 40 across the 10 of their papers we have counts for
4 papers · 1 filter
Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
Xavier Diaz, Gianluca D'Amico, Raul Dominguez-Sanchez +3
In recent years, interest in automatic train operations has significantly increased. To enable advanced functionalities, robust vision-based algorithms are essential for perceiving…
Vision Foundation Model Embedding-Based Semantic Anomaly Detection
Max Peter Ronecker, Matthew Foutter, Amine Elhafsi +4
Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonom…
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…
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…