Computational Optimal Transport [Half-Day Course]
DescriptionOver the previous decade, optimal transport (OT) has quickly become a central topic in graphics, imaging, and machine learning. OT provides a powerful and flexible way to compare, interpolate, and morph probability measures. It is an important ingredient that can be applied to diverse tasks in computer graphics, such as BRDF interpolation, shape matching, surface interpolation, shortest path computation, fluid solvers, color grading, and meshing. OT is underpinned by plentiful mathematical formalism, with the attention of the community currently geared to solve OT-related optimization problems at large scales.
This course will review the theoretical foundations and scalable formulations for OT, focusing on deploying them in computer graphics applications. One salient feature of this course is to expose OT as an interface between computer graphics and machine learning. It will interleave exposition of the mathematical framework with practical considerations, in particular deployment on GPUs using efficient yet simple Python deep learning libraries.