  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.
