Spectral methods for 3D data analysis

Speaker:  Michael Bronstein – Lugano, Switzerland
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


In recent years, geometric data is gaining increasing interest both in the academia and industry. In computer graphics and vision, this interest is owed to the rapid development of 3D acquisition and printing technologies, as well as the explosive growth of publicly-available 3D shape repositories. In machine learning, there is a gradual understanding that geometric structure plays an important role in high-dimensional complicated datasets. In this talk, I will overview some classical and most recent results in the analysis of geometric data based on spectral methods. As applications, I will showcase the problems of 3D shape descriptors, manifold correspondence, and data clustering. 

About this Lecture

Number of Slides:  55
Duration:  55 minutes
Languages Available:  English, Hebrew, Italian, Russian
Last Updated: 

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