F. Nesti

Achieving up to 20x faster anomaly segmentation for autonomous vehicles could revolutionize onboard safety systems.

Accurate railway obstacle detection is now possible thanks to a modular framework that fuses LiDAR and monocular depth estimation, achieving sub-meter distance accuracy.

Bridging the sim-to-real gap in railway perception, a new augmented reality framework generates realistic training data by seamlessly integrating virtual objects into real-world railway scenes, outperforming traditional methods.