We present a novel technique that produces two-dimensional low-discrepancy (LD) blue noise point sets for sampling. Using one-dimensional binary van der Corput sequences, we construct two-dimensional LD point sets, and rearrange them to match a target spectral profile without loosing their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.
@article{dcoeurjo_SIGASIA16,
author = {Ahmed, Abdalla G.M. and Perrier, Hélène and Coeurjolly, David and Ostromoukhov, Victor and Guo, Jianwei and Yan, Dong-Ming and HUANG, Hui and Deussen, Oliver},
doi = {10.1145/2980179.2980218},
hal_id = {hal-01372542},
journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia)},
keywords = {Quasi-Monte Carlo Methods QMC ; Blue Noise ; Low
Discrepancy ; Sampling ; Monte Carlo},
number = {6},
pages = {247:1--247:13},
pdf = {https://hal.archives-ouvertes.fr/hal-01372542/file/final-paper.pdf},
title = {Low-Discrepancy Blue Noise Sampling},
volume = {35},
year = {2016}
}