Image To Sphere Learning Equivariant

Media Summary: Recorded talk for ICLR'23 in Kigali, Rwanda. Abstract: Predicting the pose of objects from a single Episode 6: In this episode, we explore ML models that have Authors: Qin Yang, Chenglin Li, Wenrui Dai, Junni Zou, Guo-Jun Qi, Hongkai Xiong Description: Convolutional neural networks ...

Image To Sphere Learning Equivariant - Detailed Analysis & Overview

Recorded talk for ICLR'23 in Kigali, Rwanda. Abstract: Predicting the pose of objects from a single Episode 6: In this episode, we explore ML models that have Authors: Qin Yang, Chenglin Li, Wenrui Dai, Junni Zou, Guo-Jun Qi, Hongkai Xiong Description: Convolutional neural networks ... Authors: Hart, David M*; Whitney, Michael; Morse, Bryan S Description: We present a new and general framework for ... A brief overview of our paper on Scalable and ECCV 2018 Authors: Nichita Diaconu*, Daniel E. Worrall* Paper: Code: ...

Become The AI Epiphany Patreon ❤️ ‍ ‍ ‍ Join our Discord community ... Presentation O-4A-04 of European Conference on Computer Vision 2018, Munich Germany Webpage: Title: ... Seminar delivered to the UCL CDT in Foundational AI by Jason McEwen ( Paper: ... Li-Yi Wei, Arjun V Anand, Shally Kumar, Tarun Beri SIGGRPAH Asia 2020 technical communications. Ever wanted to do a convolution on a Klein Bottle? This paper defines CNNs over manifolds such that they are independent of ... This video was produced to help the readers to better understand the experiments in our paper,

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Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction (ICLR'23)

Equivariant Models | Open Catalyst Intro Series | Ep. 6

Rotation Equivariant Graph Convolutional Network for Spherical Image Classification

Interpolated SelectionConv for Spherical Images and Surfaces

Scalable and Equivariant Spherical CNNs (ICLR 2023)

CubeNet: Equivariance to 3D Rotation and Translation

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation Paper Explained

Learning SO(3) Equivariant Representations with Spherical CNNs

Geometric Deep Learning on the Sphere: Efficient Generalized Spherical CNNs (CDT in Foundational AI)

Simple Methods to Represent Shapes with Sample Spheres

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

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Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction (ICLR'23)

Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction (ICLR'23)

Recorded talk for ICLR'23 in Kigali, Rwanda. Abstract: Predicting the pose of objects from a single

Equivariant Models | Open Catalyst Intro Series | Ep. 6

Equivariant Models | Open Catalyst Intro Series | Ep. 6

Episode 6: In this episode, we explore ML models that have

Rotation Equivariant Graph Convolutional Network for Spherical Image Classification

Rotation Equivariant Graph Convolutional Network for Spherical Image Classification

Authors: Qin Yang, Chenglin Li, Wenrui Dai, Junni Zou, Guo-Jun Qi, Hongkai Xiong Description: Convolutional neural networks ...

Interpolated SelectionConv for Spherical Images and Surfaces

Interpolated SelectionConv for Spherical Images and Surfaces

Authors: Hart, David M*; Whitney, Michael; Morse, Bryan S Description: We present a new and general framework for ...

Scalable and Equivariant Spherical CNNs (ICLR 2023)

Scalable and Equivariant Spherical CNNs (ICLR 2023)

A brief overview of our paper on Scalable and

CubeNet: Equivariance to 3D Rotation and Translation

CubeNet: Equivariance to 3D Rotation and Translation

ECCV 2018 Authors: Nichita Diaconu*, Daniel E. Worrall* Paper: https://arxiv.org/abs/1804.04458 Code: ...

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

Website: https://yilundu.github.io/ndf/ Code: https://github.com/anthonysimeonov/ndf_robot Colab: ...

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation Paper Explained

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation Paper Explained

Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany ‍ ‍ ‍ Join our Discord community ...

Learning SO(3) Equivariant Representations with Spherical CNNs

Learning SO(3) Equivariant Representations with Spherical CNNs

Presentation O-4A-04 of European Conference on Computer Vision 2018, Munich Germany Webpage: https://eccv2018.org Title: ...

Geometric Deep Learning on the Sphere: Efficient Generalized Spherical CNNs (CDT in Foundational AI)

Geometric Deep Learning on the Sphere: Efficient Generalized Spherical CNNs (CDT in Foundational AI)

Seminar delivered to the UCL CDT in Foundational AI by Jason McEwen (http://www.jasonmcewen.org/). Paper: ...

Simple Methods to Represent Shapes with Sample Spheres

Simple Methods to Represent Shapes with Sample Spheres

Li-Yi Wei, Arjun V Anand, Shally Kumar, Tarun Beri SIGGRPAH Asia 2020 technical communications.

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

Ever wanted to do a convolution on a Klein Bottle? This paper defines CNNs over manifolds such that they are independent of ...

Experiment video for Equivariant Descriptor Fields (EDFs)

Experiment video for Equivariant Descriptor Fields (EDFs)

This video was produced to help the readers to better understand the experiments in our paper,