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This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, thisdoi:10.3389/frobt.2021.606770 pmid:34055900 pmcid:PMC8155491 fatcat:chco7wr3efegbpra6hw6fnu3tm