论文标题
可扩展的多级抽样通过过滤切片的最佳传输
Scalable multi-class sampling via filtered sliced optimal transport
论文作者
论文摘要
我们提出了基于连续的Wasserstein Barycenters的多类优化公式。我们的配方旨在处理数百到数千个优化目标，并带有实用的优化方案。我们证明了框架对各种采样应用的有效性，例如点画，对象放置和蒙特卡洛集成。我们是一个导出的多级误差，用于感知渲染误差，可以使用我们的优化最小化。我们在https://github.com/iribis/filtered-sliced-optimal-transport上提供源代码。
We propose a multi-class point optimization formulation based on continuous Wasserstein barycenters. Our formulation is designed to handle hundreds to thousands of optimization objectives and comes with a practical optimization scheme. We demonstrate the effectiveness of our framework on various sampling applications like stippling, object placement, and Monte-Carlo integration. We a derive multi-class error bound for perceptual rendering error which can be minimized using our optimization. We provide source code at https://github.com/iribis/filtered-sliced-optimal-transport.
