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Geometry and learning in 3D correspondence problems

Presented by: 
Alex Bronstein
Date: 
Thursday 14th December 2017 - 11:30 to 12:30
Venue: 
INI Seminar Room 1
Abstract: 
The need to compute correspondence between three-dimensional objects is a fundamental ingredient in numerous computer vision and graphics tasks. In this talk, I will show how several geometric notions related to the Laplacian spectrum provide a set of tools for efficiently calculating correspondence between deformable shapes. I will also show how this framework combined with recent ideas in deep learning promises to bring correspondence problems to new levels of accuracy.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons