Optimal Transportation for Practical Geometric Problems
Justin Solomon, MIT
Optimal transportation (OT) has gained considerable popularity as a
tool for relating signals defined over geometric domains.
Despite recent progress developing generic machinery for
understanding and optimizing OT problems, considerable effort is
still required to transition OT from a theoretical challenge to a
practical tool in the computer graphics, geometry processing, and
machine learning toolboxes. To this end, I will describe
several efforts to develop efficient, resilient OT-based algorithms
tailored to these application areas, including extensions to surface
matching, semi-supervised learning, and image processing.