DPGLA: Bridging the Gap between Synthetic and Real Data for Unsupervised Domain Adaptation in 3D LiDAR Semantic Segmentation

arXiv — cs.CV•Tuesday, October 28, 2025 at 4:00:00 AM

A new study introduces DPGLA, a method that enhances the use of synthetic data for improving LiDAR point cloud semantic segmentation in autonomous systems. This approach addresses the high costs associated with annotating real-world data by effectively utilizing unlabeled data, which could lead to more efficient and accurate autonomous technologies. The significance of this research lies in its potential to bridge the gap between synthetic and real data, making advancements in autonomous systems more accessible and practical.

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