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 2022

    I'm very happy to be part of 3 papers accepted at NeurIPS 2022:

    • Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching with Lei Li and Nicolas Donati,
    • Neural Correspondence Prior for Effective Unsupervised Shape Matching with Souhaib Attaiki
    • Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching with Ramana Subramanyam Sundararaman, Riccardo Marin and Emanuele Rodolà

    Our paper Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization with Robin Magnet, Jing Ren and Olga Sorkine-Hornung has won the best paper award at 3DV2022. Congratulations to Robin and Jing!

August 2022

    We have two papers accepted at 3DV 2022: Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization (as an oral) with Robin Magnet, Jing Ren and Olga Sorkine-Hornung and SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence (as a poster) with Lei Li and Souhaib Attaiki. These papers present, respectively, a novel approach for promoting pointwise map smoothness in functional map computations, and a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust non-rigid matching.

July 2022

    Our paper on Implicit field supervision for robust non-rigid shape matching with Ramana Sundararaman and Gautam Pai has been accepted to ECCV 2022 as an oral. This paper presents a very robust learning-based method for non-rigid shape correspondence, using the neural field shape representation.

June 2022

    Marie-Julie Rakotosaona, who did her PhD in our group, has won the best thesis award from GdR IG-RV, the French association for Computer Graphics, Virtual Reality and Visualization. In addition, she also won the best thesis second prize award from the IDIA department. Congratulations to Marie-Julie!

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