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cs.CV2025
DPGLA: Bridging the Gap between Synthetic and Real Data for Unsupervised Domain Adaptation in 3D LiDAR Semantic Segmentation
Wanmeng Li, Simone Mosco, Daniel Fusaro +1
Annotating real-world LiDAR point clouds for use in intelligent autonomous systems is costly. To overcome this limitation, self-training-based Unsupervised Domain Adaptation (UDA)…
cs.CV2025
Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios
Simone Mosco, Daniel Fusaro, Wanmeng Li +2
LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong perform…