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Point cloud, as a 3D representation, is widely used in autonomous driving,virtual reality (VR), and augmented reality (AR). However, traditionalcommunication systems think that the point cloud's semantic information isirrelevant to communication, which hinders the efficient transmission of pointclouds in the era of artificial intelligence (AI). This paper proposes a pointcloud based semantic communication system (PCSC), which uses AI-based encodingtechniques to extract the semantic information of the point cloud and jointsource-channel coding (JSCC) technology to overcome the distortion caused bynoise channels and solve the "cliff effect" in traditional communication. Inaddition, the system realizes the controllable coding rate without fine-tuningthe network. The method analyzes the coded semantic vector's importance anddiscards semantically-unimportant information, thereby improving thetransmission efficiency. Besides, PCSC and the recently proposed non-orthogonalmodel division multiple access (MDMA) technology are combined to design a pointcloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevantexperimental results show that the proposed method outperforms the traditionalmethod 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2metrics. In terms of transmission, the proposed method can effectively solvethe "cliff effect" in the traditional methods.

Semantic Communications System with Model Division Multiple Access and Controllable Coding Rate for Point Cloud