3 papers
cs.CV2025
Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking
Markus Käppeler, Ãzgün Ãiçek, Daniele Cattaneo +3
Camera-based 3D object detection and tracking are essential for perception in autonomous driving. Current state-of-the-art approaches often rely exclusively on either perspective-v…
cs.CV2025
Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning
Rohit Mohan, Julia Hindel, Florian Drews +3
Autonomous vehicles that navigate in open-world environments may encounter previously unseen object classes. However, most existing LiDAR panoptic segmentation models rely on close…
cs.CV2025
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds
Mohamed Abdelsamad, Michael Ulrich, Claudius Gläser +1
Masked autoencoders (MAE) have shown tremendous potential for self-supervised learning (SSL) in vision and beyond. However, point clouds from LiDARs used in automated driving are p…