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
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!
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.
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.
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!