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arXiv · 2605.15324

P-WRFGS: Pruning 3D Gaussians for Efficient Wireless Radiance Field Construction

Chenghong Bian·Meng Hua·Deniz Gunduz
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Abstract

Wireless channel modeling is a key building block for next-generation wireless systems. Predicting the channel state information (CSI) across different transmitter locations can substantially reduce the pilot and feedback overhead of conventional channel estimation. We propose P-WRFGS, an efficient wireless radiance field modeling framework built upon 3D Gaussian splatting. P-WRFGS introduces a learnable mask for each 3D Gaussian primitive to indicate its importance, which guides the pruning of less significant primitives for more efficient rendering. The model is trained using a weighted combination of rendering and regularization losses, allowing a flexible trade-off between rendering quality and efficiency. Numerical results on the $\text{NeRF}^2$ dataset demonstrate that P-WRFGS achieves up to 100$\times$ storage reduction and 7$\times$ rendering speed-up with only mild degradation in SSIM and the achievable rate. Moreover, initializing the Gaussian primitives from a 3D point cloud of the scene further improves the entire quality-efficiency trade-off.

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Chenghong Bian, Meng Hua, Deniz Gunduz. 2026-05-14. P-WRFGS: Pruning 3D Gaussians for Efficient Wireless Radiance Field Construction. https://arxiv.org/abs/2605.15324

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