Search Results for author: Adrien Poulenard

Found 5 papers, 3 papers with code

ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes

1 code implementation19 Jan 2022 Rahul Sajnani, Adrien Poulenard, Jivitesh Jain, Radhika Dua, Leonidas J. Guibas, Srinath Sridhar

ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.

3D Canonicalization 3D Geometry Perception +2

A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks.

no code implementations CVPR 2021 Adrien Poulenard, Leonidas J. Guibas

A fundamental problem in equivariant deep learning is to design activation functions which are both informative and preserve equivariance.

Vector Neurons: A General Framework for SO(3)-Equivariant Networks

3 code implementations ICCV 2021 Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, Leonidas Guibas

Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.

Multi-directional Geodesic Neural Networks via Equivariant Convolution

no code implementations1 Oct 2018 Adrien Poulenard, Maks Ovsjanikov

Our construction, which we call multi-directional geodesic convolution, or directional convolution for short, allows, in particular, to propagate and relate directional information across layers and thus different regions on the shape.

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