14 citations · 41 across the 14 of their papers we have counts for
31 papers
Equivalence Between SE(3) Equivariant Networks via Steerable Kernels and Group Convolution
Adrien Poulenard, Maks Ovsjanikov, Leonidas J. Guibas
A wide range of techniques have been proposed in recent years for designing neural networks for 3D data that are equivariant under rotation and translation of the input. Most appro…
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Ramana Sundararaman, Riccardo Marin, Emanuele Rodola +1
In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields. Different from exist…
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
Lei Li, Nicolas Donati, Maks Ovsjanikov
In this work, we present a novel non-rigid shape matching framework based on multi-resolution functional maps with spectral attention. Existing functional map learning methods all…
Affection: Learning Affective Explanations for Real-World Visual Data
Panos Achlioptas, Maks Ovsjanikov, Leonidas Guibas +1
In this work, we explore the emotional reactions that real-world images tend to induce by using natural language as the medium to express the rationale behind an affective response…
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…
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching
Nicolas Donati, Etienne Corman, Maks Ovsjanikov
State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions. M…