Search Results for author: Adrien Poulenard

Found 9 papers, 4 papers with code

Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields

1 code implementation CVPR 2023 Rohith Agaram, Shaurya Dewan, Rahul Sajnani, Adrien Poulenard, Madhava Krishna, Srinath Sridhar

We present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs).

Object Self-Supervised Learning

Equivalence Between SE(3) Equivariant Networks via Steerable Kernels and Group Convolution

no code implementations29 Nov 2022 Adrien Poulenard, Maks Ovsjanikov, Leonidas J. Guibas

Most approaches for equivariance under the Euclidean group $\mathrm{SE}(3)$ of rotations and translations fall within one of the two major categories.

Breaking the Symmetry: Resolving Symmetry Ambiguities in Equivariant Neural Networks

no code implementations29 Oct 2022 Sidhika Balachandar, Adrien Poulenard, Congyue Deng, Leonidas Guibas

We present OAVNN: Orientation Aware Vector Neuron Network, an extension of the Vector Neuron Network.

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

4 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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