59 citations · 76 across the 6 of their papers we have counts for
7 papers
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…
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,…
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…
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
Lei Li, Souhaib Attaiki, Maks Ovsjanikov
In this work, we present a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust n…
Smoothness and effective regularizations in learned embeddings for shape matching
Riccardo Marin, Souhaib Attaiki, Simone Melzi +2
Many innovative applications require establishing correspondences among 3D geometric objects. However, the countless possible deformations of smooth surfaces make shape matching a…
DPFM: Deep Partial Functional Maps
Souhaib Attaiki, Gautam Pai, Maks Ovsjanikov
We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy…
DiffusionNet: Discretization Agnostic Learning on Surfaces
Nicholas Sharp, Souhaib Attaiki, Keenan Crane +1
We introduce a new general-purpose approach to deep learning on 3D surfaces, based on the insight that a simple diffusion layer is highly effective for spatial communication. The r…