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CGGC Seminar: Optimal Transportation for Practical Geometric Problems
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Justin Solomon (lEE & CS,, MIT)
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Wednesday, 2.3.2016, 10:30
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Taub 401​
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.
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