3 citations · 5 across the 3 of their papers we have counts for
5 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…
Breaking the Symmetry: Resolving Symmetry Ambiguities in Equivariant Neural Networks
Sidhika Balachandar, Adrien Poulenard, Congyue Deng +1
Equivariant networks have been adopted in many 3-D learning areas. Here we identify a fundamental limitation of these networks: their ambiguity to symmetries. Equivariant networks…
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Congyue Deng, Or Litany, Yueqi Duan +3
Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds. Yet most proposed methods either use complex mathema…
Multi-directional Geodesic Neural Networks via Equivariant Convolution
Adrien Poulenard, Maks Ovsjanikov
We propose a novel approach for performing convolution of signals on curved surfaces and show its utility in a variety of geometric deep learning applications. Key to our construct…
Continuous and Orientation-preserving Correspondences via Functional Maps
Jing Ren, Adrien Poulenard, Peter Wonka +1
We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We…