From Particles to Fields: Reframing Photon Mapping with Continuous Gaussian Photon Fields

Accurately modeling light transport is essential for realistic image synthesis. Photon mapping captures complex global illumination effects such as caustics and specular-diffuse interactions, but dense photon storage and repeated surface gathering remain expensive when rendering multiple views, even when the photon map is reused. To accelerate multi-view rendering, we reformulate surface-lighting estimation as a continuous and reusable field. We introduce the Gaussian Photon Field (GPF), a learnable representation of surface irradiance using anisotropic 3D Gaussian primitives, enabling efficient radiance evaluation across viewpoints. GPF is initialized with photon-guided surface coverage and optimized against sparse multi-view stochastic progressive photon mapping (SPPM) radiance references. Differentiable Gaussian interpolation allows the irradiance coefficients and their spatial support to be fitted jointly through the rendered image predictions. At rendering time, the field is combined with the terminal Lambertian response and explicitly traced camera-side transport, preserving view-dependent reflection and refraction without further photon tracing or progressive refinement. Experiments on scenes with caustics and specular-diffuse interactions demonstrate high-fidelity multi-view rendering with reduced per-view computation, connecting physically-based light transport with the efficiency of reusable learned representations.