3 papers
cs.CV2025
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation
Andrew Caunes, Thierry Chateau, Vincent Frémont
Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive ann…
cs.CV2025
Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation
Andrew Caunes, Thierry Chateau, Vincent Fremont
3D semantic segmentation plays a pivotal role in autonomous driving and road infrastructure analysis, yet state-of-the-art 3D models are prone to severe domain shift when deployed…
cs.CV2025
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu +9
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X data…