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 derive a multi-class error bound for perceptual rendering error which can be minimized using our optimization.
Paper (author version)
We thank all the anonymous reviewers for their helpful comments in shaping the final version of the paper. We thank the following for scenes used in our experiments: julioras3d (chopper-titan), Mikael Hvidtfeldt Christensen (structuresynth), Greyscalegorilla (vw-van) and Eric Veach (Veach-mis). We also thanks Sponchia for the elephants image used in the supplemental.
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