不传点云特征，改传权重组合，实现高效低损无线传输
Transmit Weights, Not Features: Orthogonal-Basis Aided Wireless Point-Cloud Transmission
- 用接收端的正交特征池预测权重，生成紧凑表示
- 在不同信噪比下，带宽受限时性能显著优于现有方法
- 适合资源受限场景下的3D点云无线传输应用
深度传感器的普及大幅降低了点云获取门槛。本文提出一种基于深度联合信源信道编码（DeepJSCC）的语义无线点云传输框架。与直接传输原始特征不同，发送端在接收端侧的语义正交特征池上预测组合权重，实现紧凑表征并保障鲁棒重建。采用折叠式解码器将2D网格变形为3D结构，保持流形连续性同时保留几何保真度。系统在ModelNet40数据集上，以切比雪夫距离（CD）和正交性正则化训练，在不同信噪比（SNR）和带宽条件下评估。结果表明：在高带宽下性能媲美语义点云传输（SEPT），在带宽受限场景中表现更优，且在峰值信噪比（PSNR）和CD指标上均有稳定提升。消融实验验证了正交化与折叠先验的有效性。
原文摘要 · Abstract (English)
The widespread adoption of depth sensors has substantially lowered the barrier to point-cloud acquisition. This letter proposes a semantic wireless transmission framework for three dimension (3D) point clouds built on Deep Joint Source - Channel Coding (DeepJSCC). Instead of sending raw features, the transmitter predicts combination weights over a receiver-side semantic orthogonal feature pool, enabling compact representations and robust reconstruction. A folding-based decoder deforms a 2D grid into 3D, enforcing manifold continuity while preserving geometric fidelity. Trained with Chamfer Distance (CD) and an orthogonality regularizer, the system is evaluated on ModelNet40 across varying Signal-to-Noise Ratios (SNRs) and bandwidths. Results show performance on par with SEmantic Point cloud Transmission (SEPT) at high bandwidth and clear gains in bandwidth-constrained regimes, with consistent improvements in both Peak Signal-to-Noise Ratio (PSNR) and CD. Ablation experiments confirm the benefits of orthogonalization and the folding prior.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。