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.

翻译：无线信道上三维点云数据的可靠传输面临时变信噪比（SNR）与有限带宽的挑战。本文提出敏感度感知滤波与传输（SAFT）——一种融合Point-BERT启发编码器、敏感度引导令牌滤波（STF）模块、量化模块及SNR感知解码器的学习型传输框架，实现自适应重建。具体而言，STF模块根据信道扰动下各令牌的重建敏感度分配令牌级重要性分数。我们进一步采用仅用于训练的符号使用惩罚项来稳定离散表示，而不影响实际传输负载。在ShapeNet、ModelNet40及8iVFB数据集上的实验表明：相比分离式信源信道编码流水线（G-PCC结合LDPC与QAM）及现有学习基线方法，SAFT在几何保真度（D1/D2 PSNR）上均取得提升，并在低SNR条件下获得最大增益，凸显了有限带宽环境下鲁棒性的显著增强。