Recent spatiotemporal resampling algorithms (ReSTIR) accelerate real-time path tracing by reusing samples between pixels and frames. However, existing methods are limited by the sampling quality of path tracing, making them inefficient for scenes …
We present a novel algorithm for implementing Owen-scrambling,combining the generation and distribution of the scrambling bits in a single self-contained compact process. We employ a context-free grammar to build a binary tree of symbols, and equip …
Recent work on generalized resampled importance sampling (GRIS) enables importance-sampled Monte Carlo integration with random variable weights replacing the usual division by probability density. This enables very flexible spatiotemporal sample …
We describe a machine learning technique for reconstructing image sequences rendered using Monte Carlo methods. Our primary focus is on reconstruction of global illumination with extremely low sampling budgets at interactive rates. Motivated by …