Michael Bronstein (University of Lugano, Switzerland)
Tuesday, 13.11.2012, 11:30
Finding dense intrinsic correspondence between non-rigid shapes is a
notoriously difficult problem with many important applications in computer
graphics and pattern recognition.
In the first part of the talk, I will present a novel sparse modeling
approach to non-rigid shape matching using only the ability to detect
repeatable regions. As the input to our algorithm, we are given only two
sets of regions in two shapes; no descriptors are provided so the
correspondence between the regions is not know, nor we know how many
regions correspond in the two shapes. I will show that even with such
scarce information, it is possible to establish very accurate
correspondence between the shapes by posing it as a problem of permuted
In the second part of the talk, I will show how to extend the method to the
setting of non-isometric shapes using quasi-harmonic bases constructed by
joint approximate diagonalization of Laplacian matrices.