Room “Sala Seminari” - Abacus Building (U14)
Towards general-purpose feature learning for 3D shape comparison
Speaker
Prof. Maks Ovsjanikov
Ecole Polytechnique, France
Abstract
Learning informative pointwise features is a key task in 3D shape analysis with broad applications in many downstream applications. In this talk, I will present several recent approaches for feature learning, and emphasize the influence of geometric considerations during this process. I will first describe a link between contrastive learning and non-rigid shape correspondence, informing both of these areas. I will then mention how robust and generalizable features can be trained by connecting rigid and non-rigid shape analysis. Finally, I will talk about how unsupervised feature learning can give rise to surprisingly informative and rich features that can be exploited for other problems.
Short Bio
Maks Ovsjanikov is a Professor at Ecole Polytechnique in France. He works on 3D shape analysis with emphasis on deep learning techniques for shape matching and correspondence. He has received a Eurographics Young Researcher Award, an ERC Starting Grant, a CNRS Bronze Medal, and an ERC Consolidator Grant in 2023. His works have received 11 best paper awards or nominations at top conferences, including CVPR, ICCV, 3DV, as well as a SIGGRAPH 2023 Test-of-time award. His main research topics include 3D shape comparison and deep learning on 3D data.
contact person for this Seminar: simone.melzi@unimib.it