Point cloud, as a 3D representation, is widely used in autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems think that the point cloud's semantic information is irrelevant to communication, which hinders the efficient transmission of point clouds in the era of artificial intelligence (AI). This paper proposes a point cloud based semantic communication system (PCSC), which uses AI-based encoding techniques to extract the semantic information of the point cloud and joint source-channel coding (JSCC) technology to overcome the distortion caused by noise channels and solve the "cliff effect" in traditional communication. In addition, the system realizes the controllable coding rate without fine-tuning the network. The method analyzes the coded semantic vector's importance and discards semantically-unimportant information, thereby improving the transmission efficiency. Besides, PCSC and the recently proposed non-orthogonal model division multiple access (MDMA) technology are combined to design a point cloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevant experimental results show that the proposed method outperforms the traditional method 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2 metrics. In terms of transmission, the proposed method can effectively solve the "cliff effect" in the traditional methods.

翻译：点云作为一种三维表示，广泛应用于自动驾驶、虚拟现实（VR）和增强现实（AR）等领域。然而，传统通信系统认为点云的语义信息与通信无关，这阻碍了人工智能（AI）时代点云的高效传输。本文提出一种基于点云的语义通信系统（PCSC），该系统利用基于AI的编码技术提取点云的语义信息，并采用联合信源信道编码（JSCC）技术克服噪声信道引起的失真，同时解决传统通信中的“悬崖效应”。此外，该系统无需微调网络即可实现可控编码速率。该方法通过分析编码语义向量的重要性，舍弃语义不重要的信息，从而提升传输效率。进一步地，将PCSC与近期提出的非正交模型分割多址接入（MDMA）技术相结合，设计了面向多用户传输的点云MDMA传输系统（M-PCSC）。相关实验结果表明，在PSNR D1和PSNR D2指标下，所提方法在相同信道带宽比下相较于传统方法性能提升10dB。在传输方面，所提方法能够有效解决传统方法中的“悬崖效应”。