IEEE Trans Vis Comput Graph. 2017 Dec;23(12):2496-2508. doi: 10.1109/TVCG.2016.2641963. Epub 2016 Dec 19.
We describe a simple push-pull optimization (PPO) algorithm for blue-noise sampling by enforcing spatial constraints on given point sets. Constraints can be a minimum distance between samples, a maximum distance between an arbitrary point and the nearest sample, and a maximum deviation of a sample's capacity (area of Voronoi cell) from the mean capacity. All of these constraints are based on the topology emerging from Delaunay triangulation, and they can be combined for improved sampling quality and efficiency. In addition, our algorithm offers flexibility for trading-off between different targets, such as noise and aliasing. We present several applications of the proposed algorithm, including anti-aliasing, stippling, and non-obtuse remeshing. Our experimental results illustrate the efficiency and the robustness of the proposed approach. Moreover, we demonstrate that our remeshing quality is superior to the current state-of-the-art approaches.
我们描述了一种简单的推拉优化（PPO）算法，用于通过对给定的点集施加空间约束来进行蓝噪声采样。约束可以是样本之间的最小距离、任意点与最近样本之间的最大距离以及样本容量（Voronoi 胞腔的面积）与平均值的最大偏差。所有这些约束都基于 Delaunay 三角剖分产生的拓扑结构，并且可以结合使用以提高采样质量和效率。此外，我们的算法还提供了在不同目标（例如噪声和混叠）之间进行权衡的灵活性。我们提出了该算法的几个应用，包括抗混叠、点状和非钝角重网格化。我们的实验结果说明了所提出方法的效率和鲁棒性。此外，我们还证明了我们的重网格化质量优于当前的最先进方法。
