发表机构
Lomonosow Moscow State University（莫斯科国立罗蒙诺索夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种针对镜面光照定制的神经辐射度缓存变体，通过反射方向参数化等技术，在Bunny等数据集上实现更快收敛与更高镜面光照质量，且保持实时性能。
AI 中文摘要
神经辐射度缓存（NRC）为实时路径追踪提供场景光照的在线神经表示。本文提出一种针对镜面光照定制的NRC变体，采用反射方向参数化和依赖粗糙度的辐射度目标。我们的网络预测单个表面点处的预过滤入射辐射度，结合预计算的BRDF积分图，利用拆分和近似估计出射辐射度。在Bunny和Specular Sponza上的对比显示，与评估的NRC基线相比，该方法收敛到累积路径追踪参考的速度更快，镜面光照质量更高。我们的在线训练方法保持实时性能，同时在渲染过程中调整其缓存。
英文摘要
Neural Radiance Caching (NRC) provides an online neural representation of scene illumination for real-time path tracing. This paper presents an NRC variant tailored to specular lighting through a reflection-direction parameterization and a roughness-dependent radiance target. Our network predicts prefiltered incoming radiance at individual surface points, which is combined with a precomputed BRDF integration map using the split-sum approximation to estimate outgoing radiance. On Bunny and Specular Sponza, the reported comparisons indicate faster convergence to the accumulated path-tracing reference and improved specular-lighting quality relative to the evaluated NRC baselines. Our online-trained method maintains real-time performance while adapting its cache during rendering.
