  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.
