Abstract
Learning deformable shape correspondence
Alex Bronstein
Technion - Israel Institute of Technology
In this talk, I will show several learning-based approaches to tackle deformable shape correspondence problems. I will also present a completely unsupervised way of learning correspondence overcoming the need for the expensive annotated data. I will showcase these methods on a wide selection of correspondence benchmarks, where they outperform other techniques in terms of accuracy, generalization error, and efficiency.