Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction
Jinghan Zhang, Xitao Gong, Qi Wang, Richard A. Stirling-Gallacher, Giuseppe Caire

TL;DR
This paper introduces BiSplat-WRF, a novel planar Gaussian splatting framework with a bilinear spatial transformer for improved wireless radiance field reconstruction, capturing electromagnetic interactions more accurately.
Contribution
It presents a physically interpretable, globally aware Gaussian splatting method that incorporates electromagnetic coupling and mutual scattering for better wireless environment modeling.
Findings
BiSplat-WRF outperforms NeRF-based and prior GS-based methods in SSIM for spectrum synthesis.
The bilinear spatial transformer effectively captures long-range electromagnetic dependencies.
The larger BiSplat-WRF+ variant achieves higher SSIM at increased computational cost.
Abstract
Wireless radiance field (WRF) reconstruction aims to learn a continuous, queryable representation of radio frequency characteristics over 3D space and direction, from which specific quantities, such as the spatial power spectrum (SPS) at a receiver given a transmitter position, can be predicted. While Gaussian splatting (GS)-based method has surpassed Neural Radiance Fields (NeRF)-based method for this task, existing adaptations largely transplant vision pipelines, limiting physical interpretability and accuracy. We introduce BiSplat-WRF, a planar GS framework that retains the expressiveness of 3D GS while removing unnecessary projections and incorporating global EM coupling and mutual scattering among primitives. Each primitive is a 2D planar Gaussian with 3D coordinates, rendered directly on the angular domain of the SPS. A bilinear spatial transformer (BST) aggregates inter-primitive…
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