Fast acquisition technology and broad availability of 3D data underscore the need for advanced tools that process and analyze 3D shapes. Unlike image and signal processing which handle functions on flat domains with well-developed tools for processing and learning, 3D shapes present unique challenges due to their irregular and weak structure.
Despite breakneck progress in the development of tools for these tasks, many challenges remain in automatically analyzing, processing, and understanding 3D geometry. In particular, recent advances in machine learning have shown advancement in signal and image processing, while the processing of 3D shapes is less developed. This workshop aims to bring world-leading researchers in mathematics and computer science to study, explore, collaborate, and develop new ideas and research directions in combining traditional 3D shape analysis with recent developments of learning.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Mirela Ben Chen
(Technion - Israel Institute of Technology)
Ron Kimmel (Technion - Israel Institute of Technology, Intel Perceptual Computing)
Rongjie Lai (Rensselaer Polytechnic Institute)
Martin Rumpf (Rheinische Friedrich-Wilhelms-Universität Bonn)
Justin Solomon (Massachusetts Institute of Technology)