Maks Ovsjanikov

I am a professor in the Computer Science department at Ecole Polytechnique in France. I am a member of the GeoViC group at the LIX research laboratory of Ecole Polytechnique and an associate member (external collaborator) of the DataShape team at INRIA. My current research is mainly supported by my ERC Starting Grant: EXPROTEA.

My research is primarily related to geometric (3D) shape analysis with emphasis on Deep Learning for non-rigid shape comparison and processing. In the past, I have worked on topics including shape classification and retrieval, non-rigid shape-matching, comparison, denoising and symmetry detection especially on 3D point cloud and triangle mesh data. I'm also very interested in image processing, Computer Graphics and Computer Vision in general. You can find some of my work on the Publications page.

Recent News

September 2023

I'm excited to be giving a keynote at 3DOR 2023 on Efficient, general-purpose feature learning for 3D shape comparison.

I'm happy to be part of three accepted at ICCV 2023:

• SATR: Zero-Shot Semantic Segmentation of 3D Shapes with Ahmed Abdelreheem, Ivan Skorokhodov, and Peter Wonka (from KAUST);
• Spatially and Spectrally Consistent Deep Functional Maps with Mingze Sun, Shiwei Mao, Puhua Jiang, and Ruqi Huang (from TBSI); and
• VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams with Nissim Maruani, Roman Klokov, Pierre Alliez, and Mathieu Desbrun

August 2023

I'm happy and honored to have become a fellow of ELLIS, the European Laboratory for Learning and Intelligent Systems, bringing together top AI researchers in Europe.

July 2023

Our work on Functional Maps has received the ACM SIGGRAPH 2023 Test-of-Time award.

Etienne Corman, who did his PhD in our group in 2013 - 2016 just won the SMI Young Researcher Award. Congratulations to Etienne!

June 2023

Our paper ReVISOR: ResUNets with visibility and intensity for structured outlier removal with Maxime Kirgo, and collaborators from EDF R&D Guillaume Terrasse and Guillaume Thibault was accepted at the ISPRS Journal of Photogrammetry and Remote Sensing. This paper presents a 3D deep learning-based method for structured outlier detection, especially arising from reflections in laser scans of large 3D scenes.