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

Junlin Chang, Yubo Han, Hnag Yue, John S Thompson, Rongke Liu
12/3/2025
cs.LG

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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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Cite this paper

@article{chang2025transmit,
  title  = {Transmit Weights, Not Features: Orthogonal-Basis Aided Wireless Point-Cloud Transmission},
  author = {Junlin Chang and Yubo Han and Hnag Yue and John S Thompson and Rongke Liu},
  year   = {2025},
  eprint = {2512.03819},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.03819v1}
}

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