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arXiv · 2211.04314

Scalable multi-class sampling via filtered sliced optimal transport

Corentin Salaün·Iliyan Georgiev·Hans-Peter Seidel·Gurprit Singh
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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.

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Corentin Salaün, Iliyan Georgiev, Hans-Peter Seidel, Gurprit Singh. 2022-11-08. Scalable multi-class sampling via filtered sliced optimal transport. https://doi.org/10.1145/3550454.3555484%2010.1145%2F3550454.3555484%2010.1145%2F3550454.3555484%2010.1145%2F3550454.3555484%2010.1145%2F3550454.3555484%2010.1145%2F3550454.3555484

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