根据点云敏感度动态过滤传输，提升无线环境下的3D点云重建质量。
SAFT: Sensitivity-Aware Filtering and Transmission for Adaptive 3D Point Cloud Communication over Wireless Channels
- 按点云各部分对失真的敏感度分配重要性，只传关键信息
- 在低信噪比下性能优于传统编码与现有学习方法，几何保真度提升显著
- 适合资源受限的无线3D点云实时通信场景
由于信道时变信噪比（SNR）和带宽有限，无线环境下可靠传输3D点云极具挑战。本文提出敏感度感知过滤与传输（SAFT），一种融合Point-BERT式编码器、敏感度引导标记过滤（STF）模块、量化单元及信噪比感知解码器的端到端学习传输框架。其中，STF模块基于各标记在信道扰动下的重建敏感度，分配其重要性得分。我们还引入仅训练阶段生效的符号使用惩罚，稳定离散表示但不影响实际传输数据量。在ShapeNet、ModelNet40和8iVFB数据集上的实验表明，相较于独立源-信道编码方案（如G-PCC+LDPC+QAM）及现有学习基线，SAFT在几何保真度（D1/D2 PSNR）上表现更优，尤其在低信噪比条件下增益最大，验证了其在带宽受限下的鲁棒性优势。
原文摘要 · Abstract (English)
Reliable transmission of 3D point clouds over wireless channels is challenging due to time-varying signal-to-noise ratio (SNR) and limited bandwidth. This paper introduces sensitivity-aware filtering and transmission (SAFT), a learned transmission framework that integrates a Point-BERT-inspired encoder, a sensitivity-guided token filtering (STF) unit, a quantization block, and an SNR-aware decoder for adaptive reconstruction. Specifically, the STF module assigns token-wise importance scores based on the reconstruction sensitivity of each token under channel perturbation. We further employ a training-only symbol-usage penalty to stabilize the discrete representation, without affecting the transmitted payload. Experiments on ShapeNet, ModelNet40, and 8iVFB show that SAFT improves geometric fidelity (D1/D2 PSNR) compared with a separate source--channel coding pipeline (G-PCC combined with LDPC and QAM) and existing learned baselines, with the largest gains observed in low-SNR regimes, highlighting improved robustness under limited bandwidth.
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