Semantic Communication for Efficient Point Cloud Transmission

Shangzhuo Xie, Qianqian Yang, Yuyi Sun, Tianxiao Han, Zhaohui Yang, Zhiguo Shi
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Abstract

As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper, we present a novel semantic communication (SemCom) approach for efficient 3D point cloud transmission. Different from existing methods that rely on downsampling and feature extraction for compression, our approach utilizes a parallel structure to separately extract both global and local information from point clouds. This system is composed of five key components: local semantic encoder, global semantic encoder, channel encoder, channel decoder, and semantic decoder. Our numerical results indicate that this approach surpasses both the traditional Octree compression methodology and alternative deep learning-based strategies in terms of reconstruction quality. Moreover, our system is capable of achieving high-quality point cloud reconstruction under adverse channel conditions, specifically maintaining a reconstruction quality of over 37dB even with severe channel noise.
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高效点云传输的语义通信
随着激光雷达(LiDAR)相机等三维采集技术的发展,高效传输三维点云的需求变得越来越重要。在本文中,我们提出了一种用于高效传输三维点云的新型语义通信(SemCom)方法。与现有的依靠降低采样率和特征提取进行压缩的方法不同,我们的方法利用并行结构分别提取点云的全局和局部信息。该系统由五个关键部分组成:局部语义编码器、全局语义编码器、信道编码器、信道解码器和语义解码器。我们的数值结果表明,这种方法在重建质量方面超越了传统的八叉树压缩方法和基于深度学习的其他策略。此外,我们的系统还能在不利的信道条件下实现高质量的点云重建,特别是在严重的信道噪声下仍能保持超过 37dB 的重建质量。
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