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Recent work has shown that the error of Monte-Carlo rendering is visuallymore acceptable when distributed as blue-noise in screen-space. Despite recentefforts, building a screen-space sampler is still an open problem. In thistalk, we present the lessons we learned while improving our previousscreen-space sampler. Specifically: we advocate for a new criterion to assessthe quality of such samplers; we introduce a new screen-space sampler based onrank-1 lattices; we provide a parallel optimization method that is compatiblewith a GPU implementation and that achieves better quality; we detail thepitfalls of using such samplers in renderers and how to cope with manydimensions; and we provide empirical proofs of the versatility of theoptimization process.

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