SAFT: Sensitivity-Aware Filtering and Transmission for Adaptive 3D Point Cloud Communication over Wireless Channels

Abstract

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.

Figures (13)

• Figure 1: Overview of the proposed SAFT.
• Figure 2: Detailed architecture of the transformer-based point-token encoder.
• Figure 3: Sensitivity scoring MLP. The module estimates token-wise sensitivity scores $\mathbf{S}\in[0,1]$ from $\mathbf{Z}$.
• Figure 4: SNR embedding module.
• Figure 5: Detailed architecture of the refinement-based decoder for 3D point cloud reconstruction.