Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Channel Modeling in Quasi-Static Wireless Environments
TL;DR
Photon Splatting is presented, a physics-guided neural surrogate for wireless channel modeling in complex quasi-static environments that generalizes to unseen Tx locations, Rx positions, and antenna beam patterns without retraining.
Abstract
We present Photon Splatting, a physics-guided neural surrogate for wireless channel modeling in complex quasi-static environments. The work represents wave-environment interactions using surface-attached virtual sources, or photons, which carry directional wave signatures informed by the scene geometry and transmitter (Tx) configuration. At runtime, channel impulse responses (CIRs) are predicted by splatting photon contributions onto the angular domain of the receiver (Rx) using a geodesic rasterizer and aggregating them in delay. The model is trained to learn a physically grounded representation that maps Tx–Rx configurations to full channel responses. After a one-time offline training phase, the surrogate generalizes to unseen Tx locations, Rx positions, and antenna beam patterns without retraining. We demonstrate the framework on Sionna ray-tracing data for canonical 3-D scenes and a complex indoor café with 1000 Rxs. The results show millisecond-level inference latency and accurate CIR predictions across a range of configurations.