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

• URL: http://arxiv.org/abs/2512.03819v1
• Date: Wed, 03 Dec 2025 14:07:47 GMT
• Title: Transmit Weights, Not Features: Orthogonal-Basis Aided Wireless Point-Cloud Transmission
• Abstract summary: This letter proposes a semantic wireless transmission framework for 3D point clouds built on Deep Joint Source - Channel Coding (DeepJSCC)<n>Instead of sending raw features, the transmitter predicts combination weights over a receiver-side semantic orthogonal feature pool, enabling compact representations and robust reconstruction.<n>A folding-based decoder deforms a 2D grid into 3D, enforcing manifold continuity while preserving geometric fidelity.
• Score: 9.853100066584341
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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