Radio-Frequency Inverse Rendering for Wireless Environment Modeling

TL;DR

This paper introduces a physically grounded RF inverse rendering framework that decouples RF sources, scene geometry, and material properties, enabling improved wireless environment modeling and scene manipulation.

Contribution

It proposes a novel RF-aware bidirectional scattering function integrated into Gaussian splatting, allowing explicit physical attribute encoding and generalizing multiple RF tasks.

Findings

Significant performance improvements in RF tasks

Effective decoupling of RF emission, geometry, and material properties

Enhanced wireless scene editability

Abstract

Neural rendering paradigms have recently emerged as powerful tools for radio frequency (RF). However, by entangling RF sources with scene geometry and material properties, existing approaches limit downstream manipulation of scene geometry, wireless system configuration, and RF reasoning. To address this, we propose a physically grounded RF inverse rendering (RFIR) framework that explicitly decouples RF emission, geometry, and material electromagnetic properties. Our key insight is an RF-aware bidirectional scattering distribution function, embedded into the Gaussian splatting paradigm as an RF rendering equation. Each Gaussian primitive is endowed with intrinsic physical attributes, including surface normals, material electromagnetic parameters, and roughness, and leveraged by a customized ray-tracing scheme to represent RF signal synthesis. The proposed RFIR generalizes three typical RF tasks: radar cross-section synthesis, received signal strength indicator prediction, and wireless scene editability. Experiments demonstrate significant performance advantages, underscoring the potential for wireless world modeling.