3D point cloud is a three-dimensional data format generated by LiDARs and depth sensors, and is being increasingly used in a large variety of applications. This paper presents a novel solution called SEmantic Point cloud Transmission (SEPT), for the transmission of point clouds over wireless channels with limited bandwidth. At the transmitter, SEPT encodes the point cloud via an iterative downsampling and feature extraction process. At the receiver, SEPT reconstructs the point cloud with latent reconstruction and offset-based upsampling. Extensive numerical experiments confirm that SEPT significantly outperforms the standard approach with octree-based compression followed by channel coding. Compared with a more advanced benchmark that utilizes state-of-the-art deep learning-based compression techniques, SEPT achieves comparable performance while eliminating the cliff and leveling effects. Thanks to its improved performance and robustness against channel variations, we believe that SEPT can be instrumental in collaborative sensing and inference applications among robots and vehicles, particularly in the low-latency and high-mobility scenarios.

翻译：三维点云是由激光雷达和深度传感器生成的三维数据格式，正被广泛应用于各类场景。本文提出了一种名为语义点云传输（SEPT）的创新方案，用于在带宽受限的无线信道上传输点云数据。在发射端，SEPT通过迭代下采样与特征提取过程对点云进行编码；在接收端，SEPT通过潜在重建与基于偏移的上采样实现点云重构。大量数值实验表明，SEPT显著优于基于八叉树压缩加信道编码的标准方案。与采用最先进深度学习压缩技术的基准方案相比，SEPT在消除悬崖效应与电平效应的同时达到了相当的性能。凭借其优越的性能和信道变化鲁棒性，我们相信SEPT可助力机器人与车辆间的协同感知与推理应用，尤其是在低延迟、高移动性场景中。