59 citations · 162 across the 59 of their papers we have counts for
10 papers · 1 filter
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov
A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. T…
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…
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
Robin Magnet, Jing Ren, Olga Sorkine-Hornung +1
We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to high-quality…
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…