Title: 3D Discrete Cosine Transformation-based System for 3D LiDAR Continuous Point Cloud Compression This paper proposes a method to enhance the compression of 3D LiDAR raw data by reordering point cloud packets to reduce spatial redundancy and employing efficient differential processing to minimize temporal redundancy. Importantly, the approach presented in this paper achieves notable compression results without the need for extensive training datasets, making it adaptable for practical implementation on hardware or utilizing hardware acceleration to enhance compression and decompression times. The algorithm's computational complexity is notably lower compared to other point cloud compression methods that rely on deep learning techniques.