Scalable multi-class sampling via filtered sliced optimal transport
arXiv:2211.04314 · doi:10.1145/3550454.3555484 10.1145/3550454.3555484 10.1145/3550454.3555484 10.1145/3550454.3555484 10.1145/3550454.3555484 10.1145/3550454.3555484
Abstract
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.
15 pages, 17 figures, ACM Trans. Graph., Vol. 41, No. 6, Article 261. Publication date: December 2022