Geometry and Learning in 3D Shape AnalysisMS29

With the advance of modern technology and computer power, processing and analysis of 3D shapes becomes a ubiquitous task due to fast acquisition and frequent use of 3D data. Recent developments of machine learning bring many state-of-the-art results in image processing. However, extension of deep learning methods in 3D shape analysis is still in an early state due to the challenge of non-Euclidean structures of 3D shapes. The mini symposium aims at enhancing interaction of scholars working on learning methods for 3D shape analysis.

Learning Geometry
Ron Kimmel (Technion - Israel Institute of Technology)
Geometric Interpretation to GAN model
Xianfeng Gu (State University of New York at Stony Brook )
Tradeoffs between speed and accuracy in inverse problems
Alexander Bronstein (Technion - Israel Institute of Technology)
The Geometry of Synchronization Problems and Learning Group Actions
Tingran Gao (The University of Chicago)
Rongjie Lai (Rensselaer Polytechnic Institute)
Ronald Lui (Chinese University of Hong Kong)
computer graphics, computer vision, deep learning, machine learning, nonlinear optimization