59 citations · 76 across the 6 of their papers we have counts for
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cs.CV2023
Understanding and Improving Features Learned in Deep Functional Maps
Souhaib Attaiki, Maks Ovsjanikov
Deep functional maps have recently emerged as a successful paradigm for non-rigid 3D shape correspondence tasks. An essential step in this pipeline consists in learning feature fun…
cs.CV2023
Generalizable Local Feature Pre-training for Deformable Shape Analysis
Souhaib Attaiki, Lei Li, Maks Ovsjanikov
Transfer learning is fundamental for addressing problems in settings with little training data. While several transfer learning approaches have been proposed in 3D, unfortunately,…
cs.CV2023★ 7 cited
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
Souhaib Attaiki, Maks Ovsjanikov
We present Neural Correspondence Prior (NCP), a new paradigm for computing correspondences between 3D shapes. Our approach is fully unsupervised and can lead to high-quality corres…