Blue-Noise Dithered QMC Hierarchical Russian Roulette

TL;DR

This paper introduces a novel deterministic blue-noise dithered Quasi Monte Carlo variant of hierarchical Russian roulette, enhancing the efficiency of sampling in complex light transport simulations.

Contribution

It presents a new deterministic sampling method for hierarchical Russian roulette, improving upon the original pseudo-random approach for rendering applications.

Findings

Reduced variance in sampling results

Improved computational efficiency

Enhanced rendering quality

Abstract

In order to efficiently sample specular-diffuse-glossy and glossy-diffuse-glossy transport phenomena, Tokuyoshi and Harada introduced hierarchical Russian roulette, a smart algorithm that allows to compute the minimum of the random numbers associated to leaves of a tree at each internal node. The algorithm is used to efficiently cull the connections between the product set of eye and light vertices belonging to large caches of eye and light subpaths produced through bidirectional path tracing. The original version of the algorithm is entirely based on the generation of semi-stratified pseudo-random numbers. Our paper proposes a novel variant based on deterministic blue-noise dithered Quasi Monte Carlo samples.