面向无线点云传输的深度联合信源信道编码
Deep joint source-channel coding for wireless point cloud transmission
AI总结：
本文提出 PCST 端到端无线点云语义传输系统，结合稀疏卷积渐进重采样、深度 JSCC 与自适应熵编码，在多 SNR 下优于 SSCC，并以一半以下带宽获得更好重建质量。
摘要：
通过无线网络传输高质量点云的需求日益增长，这带来了重大挑战，主要原因在于数据量庞大且需要高效的编码技术。为应对这些挑战，我们提出了一种名为 Deep Point Cloud Semantic Transmission（PCST，深度点云语义传输）的新型系统，专为端到端无线点云传输而设计。我们的方法采用基于稀疏卷积的渐进式重采样框架，将点云数据投影到语义潜在空间。随后，这些语义特征通过深度联合信源信道（JSCC）编码器进行编码，生成信道输入序列。为提高传输效率，我们使用一种基于熵的自适应方法评估每个语义特征的重要性，使传输长度能够根据其预测熵而变化。PCST 在不同信噪比（SNR）水平下均具有鲁棒性，并支持可调节的率失真（RD）权衡，从而确保灵活高效的传输。实验结果表明，PCST 显著优于传统的分离式信源信道编码（SSCC）方案，可提供更优的重建质量，同时实现超过 50% 的带宽使用降低。
英文摘要：
The growing demand for high-quality point cloud transmission over wireless networks presents significant challenges, primarily due to the large data sizes and the need for efficient encoding techniques. In response to these challenges, we introduce a novel system named Deep Point Cloud Semantic Transmission (PCST), designed for end-to-end wireless point cloud transmission. Our approach employs a progressive resampling framework using sparse convolution to project point cloud data into a semantic latent space. These semantic features are subsequently encoded through a deep joint source-channel (JSCC) encoder, generating the channel-input sequence. To enhance transmission efficiency, we use an adaptive entropy-based approach to assess the importance of each semantic feature, allowing transmission lengths to vary according to their predicted entropy. PCST is robust across diverse Signal-to-Noise Ratio (SNR) levels and supports an adjustable rate-distortion (RD) trade-off, ensuring flexible and efficient transmission. Experimental results indicate that PCST significantly outperforms traditional separate source-channel coding (SSCC) schemes, delivering superior reconstruction quality while achieving over a 50% reduction in bandwidth usage.
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