论文标题
高斯蓝噪声
Gaussian Blue Noise
论文作者
论文摘要
在用蓝噪声谱生产点分布的各种方法中，我们主张使用高斯内核进行优化框架。我们表明，通过明智的优化参数选择，这种方法具有前所未有的质量，可证明超过了最佳运输（BNOT）方法所获得的当前最新技术状态。此外，我们表明我们的算法平稳缩放到高尺寸，同时保持相同的质量，并实现前所未有的高质量高维蓝噪声集。最后，我们显示了自适应采样的扩展。
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 selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the current state of the art attained by the optimal transport (BNOT) approach. Further, we show that our algorithm scales smoothly and feasibly to high dimensions while maintaining the same quality, realizing unprecedented high-quality high-dimensional blue noise sets. Finally, we show an extension to adaptive sampling.
