We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates. The updates and cache queries incur a mild overhead -- about 2.6ms on full HD resolution -- thanks to a streaming implementation of the neural network that fully exploits modern hardware. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.

中文翻译：

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用于路径追踪的实时神经辐射缓存

我们提出了一种用于路径跟踪全局照明的实时神经辐射缓存方法。我们的系统旨在处理完全动态的场景，并且不对照明、几何形状和材料做任何假设。我们方法的数据驱动特性回避了缓存算法的许多困难，例如定位、插值和更新缓存点。由于预训练神经网络来处理新颖的动态场景是一项艰巨的泛化挑战，我们取消了预训练，而是通过适应实现泛化，即我们选择在渲染时训练辐射缓存。我们采用自我训练来提供低噪声训练目标，并通过仅迭代少弹跳训练更新来模拟无限弹跳传输。更新和缓存查询会产生轻微的开销——大约 2。全高清分辨率为 6 毫秒——这要归功于充分利用现代硬件的神经网络的流媒体实现。我们以极少的诱导偏差为代价证明了显着的降噪，并在许多具有挑战性的场景中报告了最先进的实时性能。