Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission

• URL: http://arxiv.org/abs/2506.03211v1
• Date: Tue, 03 Jun 2025 01:14:58 GMT
• Title: Channel-adaptive Cross-modal Generative Semantic Communication for Point Cloud Transmission
• Authors: Wanting Yang, Zehui Xiong, Qianqian Yang, Ping Zhang, Merouane Debbah, Rahim Tafazolli,
• Abstract summary: We propose a novel cross-modal generative semantic communication (SemCom) for PC transmission, called GenSeC-PC.<n>GenSeC-PC employs a semantic encoder that fuses images and point clouds, where images serve as non-transmitted side information.<n>To ensure robust transmission and reduce system complexity, we design a streamlined and asymmetric channel-adaptive joint semantic-channel coding architecture.
• Score: 31.144719637429567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of autonomous driving and extended reality, efficient transmission of point clouds (PCs) has become increasingly important. In this context, we propose a novel channel-adaptive cross-modal generative semantic communication (SemCom) for PC transmission, called GenSeC-PC. GenSeC-PC employs a semantic encoder that fuses images and point clouds, where images serve as non-transmitted side information. Meanwhile, the decoder is built upon the backbone of PointDif. Such a cross-modal design not only ensures high compression efficiency but also delivers superior reconstruction performance compared to PointDif. Moreover, to ensure robust transmission and reduce system complexity, we design a streamlined and asymmetric channel-adaptive joint semantic-channel coding architecture, where only the encoder needs the feedback of average signal-to-noise ratio (SNR) and available bandwidth. In addition, rectified denoising diffusion implicit models is employed to accelerate the decoding process to the millisecond level, enabling real-time PC communication. Unlike existing methods, GenSeC-PC leverages generative priors to ensure reliable reconstruction even from noisy or incomplete source PCs. More importantly, it supports fully analog transmission, improving compression efficiency by eliminating the need for error-free side information transmission common in prior SemCom approaches. Simulation results confirm the effectiveness of cross-modal semantic extraction and dual-metric guided fine-tuning, highlighting the framework's robustness across diverse conditions, including low SNR, bandwidth limitations, varying numbers of 2D images, and previously unseen objects.

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