Lessons Learned and Improvements when Building Screen-Space Samplers with Blue-Noise Error Distribution

TL;DR

This paper discusses lessons learned and improvements made in constructing screen-space samplers with blue-noise error distribution, introducing new criteria, a rank-1 lattice-based sampler, and GPU-compatible optimization methods.

Contribution

It introduces a new quality criterion, a rank-1 lattice-based sampler, and a GPU-compatible parallel optimization method for improved screen-space sampling.

Findings

Better sampler quality with the new optimization method

Versatility of the optimization process across many dimensions

Identification of pitfalls and solutions in renderer integration

Abstract

Recent work has shown that the error of Monte-Carlo rendering is visually more acceptable when distributed as blue-noise in screen-space. Despite recent efforts, building a screen-space sampler is still an open problem. In this talk, we present the lessons we learned while improving our previous screen-space sampler. Specifically: we advocate for a new criterion to assess the quality of such samplers; we introduce a new screen-space sampler based on rank-1 lattices; we provide a parallel optimization method that is compatible with a GPU implementation and that achieves better quality; we detail the pitfalls of using such samplers in renderers and how to cope with many dimensions; and we provide empirical proofs of the versatility of the optimization process.