Accurately modeling light transport is essential for realistic image synthesis. Photon mapping provides physically grounded estimates of complex global illumination effects such as caustics and specular-diffuse interactions, yet its per-view radiance estimation remains computationally inefficient when rendering multiple views of the same scene. The inefficiency arises from independent photon tracing and stochastic kernel estimation at each viewpoint, leading to inevitable redundant computation. To accelerate multi-view rendering, we reformulate photon mapping as a continuous and reusable radiance function. Specifically, we introduce the Gaussian Photon Field (GPF), a learnable representation that encodes photon distributions as anisotropic 3D Gaussian primitives parameterized by position, rotation, scale, and spectrum. GPF is initialized from physically traced photons in the first SPPM iteration and optimized using multi-view supervision of final radiance, distilling photon-based light transport into a continuous field. Once trained, the field enables differentiable radiance evaluation along camera rays without repeated photon tracing or iterative refinement. Extensive experiments on scenes with complex light transport, such as caustics and specular-diffuse interactions, demonstrate that GPF attains photon-level accuracy while reducing computation by orders of magnitude, unifying the physical rigor of photon-based rendering with the efficiency of neural scene representations.

翻译：精确建模光传输对于真实感图像合成至关重要。光子映射为复杂全局光照效应（如焦散与镜面-漫反射相互作用）提供了物理基础的估计，但在渲染同一场景的多个视角时，其逐视角辐射度估计仍存在计算效率低下的问题。这种低效源于每个视角独立的粒子追踪与随机核估计，导致不可避免的冗余计算。为加速多视角渲染，我们将光子映射重构为连续可复用的辐射度函数。具体而言，我们提出高斯光子场——一种可学习的表示方法，将光子分布编码为由位置、旋转、尺度与光谱参数化的各向异性三维高斯基元。GPF通过首次SPPM迭代中物理追踪的光子进行初始化，并利用最终辐射度的多视角监督进行优化，从而将基于光子的光传输提炼为连续场。训练完成后，该场支持沿相机光线的可微辐射度评估，无需重复的光子追踪或迭代优化。在包含焦散与镜面-漫反射相互作用等复杂光传输场景上的大量实验表明，GPF在实现光子级精度的同时，将计算量降低了数个数量级，统一了基于光子的渲染的物理严谨性与神经场景表示的高效性。