Quad-Optimized Low-Discrepancy Sequences

Abstract

The convergence of Monte Carlo integration is given by the uniformity of samples as well as the regularity of the integrand. Despite much effort dedicated to producing excellent, extremely uniform, sampling patterns, the Sobol' sampler remains unchallenged in production rendering systems. This is not only due to its reasonable quality, but also because it allows for integration in (almost) arbitrary dimension, with arbitrary sample count, while actually producing sequences thus allowing for progressive rendering, with fast sample generation and small memory footprint. We improve over Sobol' sequences in terms of sample uniformity in consecutive 2-d and 4-d projections, while providing similar practical benefitssequences, high dimensionality, speed and compactness. We base our contribution on a base-3 Sobol' construction, involving a search over irreducible polynomials and generator matrices, that produce (1, 4)-sequences or (2,4