Scalable Multi-Class Sampling via Filtered Sliced Optimal Transport
Corentin Salaün, Iliyan Georgiev, Hans-Peter Seidel, Gurprit Singh
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.
Topics & Concepts
Computer scienceScalabilityRendering (computer graphics)Class (philosophy)Mathematical optimizationMonte Carlo methodCode (set theory)Optimization problemPoint (geometry)AlgorithmMathematicsArtificial intelligenceSet (abstract data type)DatabaseStatisticsGeometryProgramming languageMedical Image Segmentation TechniquesImage and Signal Denoising MethodsMedical Imaging Techniques and Applications