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This paper presents a novel hierarchical framework for lossless LiDAR data compression, which utilizes class-aware segmentation, adaptive algorithm selection, and hierarchical compression to enhance efficiency. The method significantly reduces file sizes, achieving up to 12.76% for high-density and 22.51% for low-density point clouds, while maintaining data fidelity. Evaluated against ten benchmark datasets, the proposed approach outperforms existing techniques like LASzip, making it suitable for large-scal

Author

olizm20

Language

EN