3DGV Seminar: Maks Ovsjanikov --- Robust and Efficient Geometric DL for Non-Rigid Shape Processing

Title: Towards robust and efficient geometric deep learning for non-rigid shape processing

Abstract: In this talk I will describe several recent works for accurate and robust understanding of non-rigid 3D shapes. I will highlight several recent architectures, focusing on both spectral and spatial methods for a range of tasks including non-rigid shape matching, segmentation and reconstruction. My ultimate goal will be to show that these techniques are becoming remarkably robust and universally applicable and useful.

Bio: Maks Ovsjanikov is a Professor at Ecole Polytechnique in France. He works on 3D shape analysis with emphasis on shape matching and correspondence. He has received a Eurographics Young Researcher Award in 2014 "in recognition of his outstanding contributions to theoretical foundations of non-rigid shape matching". In 2017 he received an ERC Starting Grant from the European Commission and in 2018 a Bronze Medal from the French National Center for Scientific Research (CNRS) for research contributions in Computer Science. His main research topics include 3D shape comparison and deep learning on 3D data.