Transmit Weights, Not Features: Orthogonal-Basis Aided Wireless Point-Cloud Transmission
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 3 Dec 2025.
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References · 6
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
- 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
- 2023Visual Point Cloud Forecasting enables Scalable Autonomous Drivingcited by 99
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