Among the various approaches for producing point distributions with blue noise spectrum,
we argue for an optimization framework using Gaussian kernels. We show that with a wise …

We propose a multi-class point optimization formulation based on continuous Wasserstein
barycenters. Our formulation is designed to handle hundreds to thousands of optimization …

Many applications ranging from quasi-Monte Carlo integration over optimal control to neural
networks benefit from high-dimensional, highly uniform samples. In the case of computer …

We explore the space of matrix-generated (0, m, 2)-nets and (0, 2)-sequences in base 2,
also known as digital dyadic nets and sequences. In computer graphics, they are arguably …

We present a novel algorithm for implementing Owen-scrambling, combining the generation
and distribution of the scrambling bits in a single self-contained compact process. We …

We introduce a novel method to generate sample sequences that are progressively stratified
both in high dimensions and in lower-dimensional projections. Our method comes from a …

We propose a method for implicitly generating blue noise point sets. Our method is based on
the observations that curl noise vector fields are volume-preserving and that jittering can be …

Much effort has been put into developing samplers with specific properties, such as
producing blue noise, low-discrepancy, lattice or Poisson disk samples. These samplers can …