7 citations · 10 across the 10 of their papers we have counts for
10 papers
PoNQ: a Neural QEM-based Mesh Representation
Nissim Maruani, Maks Ovsjanikov, Pierre Alliez +1
Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applicatio…
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
Souhaib Attaiki, Maks Ovsjanikov
We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data. SNK operate…
Unsupervised Representation Learning for Diverse Deformable Shape Collections
Sara Hahner, Souhaib Attaiki, Jochen Garcke +1
We introduce a novel learning-based method for encoding and manipulating 3D surface meshes. Our method is specifically designed to create an interpretable embedding space for defor…
VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
Nissim Maruani, Roman Klokov, Maks Ovsjanikov +2
In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D surfaces remains a challenge. In particular, while polygon meshes are arguably t…
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,…