Transmit Weights, Not Features: Orthogonal-Basis Aided Wireless Point-Cloud Transmission

TL;DR

This paper introduces a novel semantic wireless transmission framework for 3D point clouds that predicts combination weights over an orthogonal feature pool, enabling efficient and robust reconstruction especially in bandwidth-limited scenarios.

Contribution

It proposes a new DeepJSCC-based method that transmits weights over an orthogonal feature pool instead of raw features, improving bandwidth efficiency and reconstruction quality.

Findings

Achieves performance comparable to existing methods at high bandwidths.

Shows significant gains in bandwidth-constrained environments.

Demonstrates the effectiveness of orthogonalization and folding prior through ablation studies.

Abstract

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.