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

TL;DR

This paper introduces GenSeC-PC, a novel semantic communication framework for point cloud transmission that combines cross-modal encoding, adaptive channel coding, and generative models to achieve high compression, robustness, and real-time performance.

Contribution

It presents a new cross-modal semantic encoding scheme, an adaptive joint coding architecture, and employs diffusion models for fast, reliable point cloud reconstruction in noisy conditions.

Findings

Outperforms existing methods in reconstruction quality.

Maintains robustness under low SNR and bandwidth constraints.

Enables real-time point cloud communication.

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.