A Geometric Algebra-Informed 3D Gaussian Splatting Framework for Wireless Scene Representation

2026-05-18

AI Summary

This work addresses the challenge of modeling ray-object interactions in complex wireless environments by proposing an end-to-end neural architecture that integrates 3D Gaussian Splatting with a geometric algebra attention mechanism. The model explicitly captures multipath propagation, attenuation, and reflection/diffraction effects of electromagnetic waves. Notably, it introduces geometric algebra into the 3D Gaussian Splatting framework for the first time, enabling joint encoding of spatial geometry and electromagnetic relationships, thereby endowing the model with physical interpretability. Evaluated on multiple real-world indoor datasets, the proposed method achieves state-of-the-art performance across various wireless signal prediction tasks, significantly outperforming existing approaches.

Abstract

In this paper, we introduce Geometric Algebra-Informed 3D Gaussian Splatting (GAI-GS), a framework for wireless modeling that couples 3D Gaussian splatting with a geometric algebra-based attention mechanism to explicitly model ray-object interactions in complex propagation environments. GAI-GS encodes joint spatial-electromagnetic (EM) relations into token representations, enabling scene-level aggregation within a unified, end-to-end neural architecture. This design grounds wireless ray propagation in electromagnetic principles, allowing token interactions to model key effects such as multipath, attenuation, and reflection/diffraction. Through extensive evaluations on multiple real-world indoor datasets, GAI-GS consistently surpasses current baselines across various wireless tasks.

Problem

• wireless scene representation
• ray-object interactions
• multipath
• attenuation
• reflection/diffraction

Innovation

• Geometric Algebra
• 3D Gaussian Splatting
• Wireless Scene Representation
• Ray-Object Interaction
• Electromagnetic Modeling