Cache

Physics

Geology

Computer network

Mathematics

Global illumination

Operating system

Optics

Path tracing

Mathematical analysis

Adaptation (eye)

Artificial intelligence

Exploit

Generalization

Rendering (computer graphics)

Radiance

Path (computing)

Overhead (engineering)

Computer science

Real-time computing

Deep neural networks

Computer security

Artificial neural network

Remote sensing

ACM Transactions on Graphics

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.

Real-time neural radiance caching for path tracing